MX2018014697A

Method and apparatus for performing signal conditioning to mitigate interference detected in a communication system.

Abstract

A system that incorporates aspects of the subject disclosure may perform operations including, for example, receiving, via an antenna, a signal generated by a communication device, detecting an interference in the signal, the interference generated by one or more transmitters unassociated with the communication device, and the interference determined from signal characteristics associated with a signaling protocol used by the one or more transmitters, and performing signal conditioning on the signal to reduce the interference. Other embodiments are disclosed.

MX2018014697A, drawing sheet 1 — Sheet 1 of 10

Term

10.7 yearsleft in the term

Expires 24 May 2037.

43 claims: 3 independent, 40 dependent

1
REIVINDICACIONES 1. Un método, caracterizado porque comprende:recibir, mediante un circuito, una señal generada por un dispositivo de comunicación;detectar, mediante el circuito, una interferencia en la señal, la interferencia generada por uno o más transmisores, y la interferencia determinada a partir de las distinciones de señal asociadas con un protocolo de señalización utilizado por uno o más transmisores, en donde uno o más transmisores no se asocian con el dispositivo de comunicación que genera la señal;y realizar, mediante el circuito, el acondicionamiento de señal sobre la señal para reducir la interferencia.
2
El método de conformidad con la reivindicación 1, caracterizado porque la detección comprende detectar en un dominio de tiempo que un nivel de potencia de la señal excede un umbral de nivel de potencia.
3
El método de conformidad con la reivindicación 2, caracterizado porque el umbral de nivel de potencia se determina de acuerdo con una relación de potencia de pico a promedio.
4
El método de conformidad con la reivindicación 3, caracterizado porque el umbral de nivel de potencia se determina a partir de las distinciones de nivel de potencia esperadas de una señal de enlace ascendente que no se somete a interferencia.
5
El método de conformidad con la reivindicación 2, caracterizado porque la detección comprende medir un perfil de dominio de tiempo de la señal y detectar la interferencia al comparar el umbral de nivel de potencia con el perfil de dominio de tiempo.
6
El método de conformidad con la reivindicación 5, caracterizado porque el perfil de dominio de tiempo se determina de acuerdo con un cálculo de media de una magnitud y nivel de potencia de la señal, un cálculo de mediana de la magnitud y nivel de potencia de la señal, un cálculo de pico de la magnitud y nivel de potencia de la señal, un cálculo de relación de potencia de pico a promedio de la señal, un cálculo de magnitud de la relación de pico a promedio de la señal, o cualquier combinación de los mismos.
7
El método de conformidad con la reivindicación 1, caracterizado porque el acondicionamiento de señal se realiza al reducir el nivel de potencia de la señal en una o más porciones de la señal en un dominio de tiempo.
8
El método de conformidad con la reivindicación 5, caracterizado porque el perfil de dominio de tiempo se mide de forma síncrona basándose en una o más distinciones de tiempo de la señal.
9
El método de conformidad con la reivindicación 8, caracterizado porque una o más distinciones de tiempo se determinan a partir del protocolo de señalización de la señal, un tiempo de símbolos de la señal, una subtrama de la señal, una trama de la señal, o cualquier combinación de las mismas.
10
El método de conformidad con la reivindicación 5, caracterizado porque el perfil de dominio de tiempo se mide de forma asincrona a partir de una o más distinciones de tiempo de la señal.
11
El método de conformidad con la reivindicación 1, caracterizado porque la interferencia comprende una interferencia de intermodulación pasiva generada por uno o más transmisores.
12
El método de conformidad con la reivindicación 1, caracterizado porque el protocolo de señalización utilizado por uno o más transmisores comprende un protocolo de multiplexión de dominio de frecuencia ortogonal.
13
El método de conformidad con la reivindicación 1, caracterizado porque la señal generada por el dispositivo de comunicación cumple con el protocolo de acceso múltiple de dominio de frecuencia de una sola portadora.
14
El método de conformidad con la reivindicación 1, caracterizado porque la detección comprende detectar en un dominio de frecuencia que un nivel de potencia de la señal excede un umbral de nivel de potencia.
15
El método de conformidad con la reivindicación 14, caracterizado porque el umbral de nivel de potencia se determina de acuerdo con un nivel de potencia promedio determinado al promediar los niveles de potencia en el dominio de la frecuencia o al agregar una compensación al nivel de potencia promedio.
16
El método de conformidad con la reivindicación 14, caracterizado porque la detección comprende medir un perfil de dominio de frecuencia de la señal y detectar la interferencia al comparar el umbral de nivel de potencia con el perfil de dominio de frecuencia.
17
El método de conformidad con la reivindicación 16, caracterizado porque el perfil de dominio de frecuencia se determina de acuerdo con un ancho de banda ocupado de la señal, una forma de una densidad espectral de potencia de la señal, una utilización de bloque de recursos de la señal, cálculo de relación de pico a promedio sobre una porción o todo un espectro de frecuencia de la señal, o cualquier combinación de los mismos.
18
El método de conformidad con la reivindicación 1, caracterizado porque el acondicionamiento de la señal se realiza al reducir un nivel de potencia de la señal en una o más porciones de la señal en un dominio de frecuencia, filtrado espacial de la señal, selección de polarización de la señal o combinaciones de los mismos.
19
El método de conformidad con la reivindicación 1, caracterizado porque el dispositivo de comunicación comprende un dispositivo de comunicación portátil.
20
El método de conformidad con la reivindicación 1, caracterizado porque el dispositivo de comunicación comprende una estación base.
21
El método de conformidad con la reivindicación 1, caracterizado porque uno o más transmisores son parte de una o más estaciones base.
22
El método de conformidad con la reivindicación 1, caracterizado porque uno o más transmisores son parte de uno o más dispositivos de comunicación portátiles.
23
El método de conformidad con la reivindicación 1, caracterizado porque la señal cumple con un protocolo de interfaz común de radio pública (CPRI).
24
El método de conformidad con la reivindicación 1, caracterizado porque la señal se origina de antenas de múltiple entrada y múltiple salida (MIMO).
25
El método de conformidad con la reivindicación 1, caracterizado porque la detección comprende detectar la interferencia en la señal basándose en el conocimiento de que uno o más transmisores está causando la interferencia.
26
Un dispositivo, caracterizado porque comprende:una antena;y un circuito acoplado a la antena, el circuito facilita las operaciones que incluyen: recibir, mediante la antena, una señal generada por un dispositivo de comunicación: detectar una interferencia en la señal, la interferencia generada por uno o más transmisores, y la interferencia determinada a partir de las distinciones de señal asociadas con un protocolo de señalización utilizado por uno o más transmisores, en donde uno o más transmisores no se asocian con el dispositivo de comunicación que genera la señal;y realizar el acondicionamiento de señal sobre la señal para reducir la interferencia.
27
El dispositivo de conformidad con la reivindicación 26, caracterizado porque la detección comprende medir un perfil de dominio de tiempo de la señal y detectar la interferencia al comparar un umbral de nivel de potencia con el perfil de dominio de tiempo.
28
El dispositivo de conformidad con la reivindicación 27, caracterizado porque el perfil de dominio de tiempo se determina de acuerdo con un cálculo de media de una magnitud y nivel de potencia de la señal, un cálculo de mediana de la magnitud y nivel de potencia de la señal, un cálculo de pico de la magnitud y nivel de potencia de la señal, un cálculo de relación de potencia de pico a promedio de la señal, un cálculo de magnitud de la relación de pico a promedio de la señal, o cualquier combinación de los mismos.
29
El dispositivo de conformidad con la reivindicación 26, caracterizado porque la detección comprende medir un perfil de dominio de frecuencia de la señal y detectar la interferencia al comparar un umbral de nivel de potencia con el perfil de dominio de frecuencia.
30
El dispositivo de conformidad con la reivindicación 29, caracterizado porque el perfil de dominio de frecuencia se determina de acuerdo con un ancho de banda ocupado de la señal, una forma de una densidad espectral de potencia de la señal, una utilización de bloque de recursos de la señal, cálculo de relación de pico a promedio sobre una porción o todo un espectro de frecuencia de la señal, o cualquier combinación de los mismos.
31
El dispositivo de conformidad con la reivindicación 26, caracterizado porque el acondicionamiento de la señal se realiza al reducir un nivel de potencia de la señal en una o más porciones de la señal en un dominio de frecuencia, filtrado espacial de la señal, selección de polarización de la señal o combinaciones de los mismos.
32
El dispositivo de conformidad con la reivindicación 26, caracterizado porque la señal cumple con un protocolo de interfaz común de radio pública (CPRI).
33
El dispositivo de conformidad con la reivindicación 26, caracterizado porque la señal se origina de antenas de múltiple entrada y múltiple salida (MIMO).
34
El dispositivo de conformidad con la reivindicación 26, caracterizado porque la detección comprende detectar la interferencia en la señal basándose en el conocimiento de que uno o más transmisores está causando la interferencia.
35
Un medio de almacenamiento legible por máquina, que comprende instrucciones ejecutables que, cuando se ejecutan por un circuito, facilitan el rendimiento de operaciones, caracterizado porque comprende:recibir, mediante una antena, una señal generada por un dispositivo de comunicación: detectar una interferencia en la señal, la interferencia generada por uno o más transmisores, y la interferencia determinada a partir de las distinciones de señal asociadas con un protocolo de señalización utilizado por uno o más transmisores, en donde uno o más transmisores no se asocian con el dispositivo de comunicación que genera la señal;y realizar el acondicionamiento de señal sobre la señal para reducir la interferencia.
36
El medio de almacenamiento legible por máquina de conformidad con la reivindicación 35, caracterizado porque la detección comprende medir un perfil de dominio de tiempo de la señal y detectar la interferencia al comparar un umbral de nivel de potencia con el perfil de dominio de tiempo.
37
El medio de almacenamiento legible por máquina de conformidad con la reivindicación 36, caracterizado porque el perfil de dominio de tiempo se determina de acuerdo con un cálculo de media de una magnitud y nivel de potencia de la señal, un cálculo de mediana de la magnitud y nivel de potencia de la señal, un cálculo de pico de la magnitud y nivel de potencia de la señal, un cálculo de relación de potencia de pico a promedio de la señal, un cálculo de magnitud de la relación de pico a promedio de la señal, o cualquier combinación de los mismos.
38
El medio de almacenamiento legible por máquina de conformidad con la reivindicación 35, caracterizado porque la detección comprende medir un perfil de dominio de frecuencia de la señal y detectar la interferencia al comparar un umbral de nivel de potencia con el perfil de dominio de frecuencia.
39
El medio de almacenamiento legible por máquina de conformidad con la reivindicación 38, caracterizado porque el perfil de dominio de frecuencia se determina de acuerdo con un ancho de banda ocupado de la señal, una forma de una densidad espectral de potencia de la señal, una utilización de bloque de recursos de la señal, cálculo de relación de pico a promedio sobre una porción o todo un espectro de frecuencia de la señal, o cualquier combinación de los mismos.
40
El medio de almacenamiento legible por máquina de conformidad con la reivindicación 35, caracterizado porque el acondicionamiento de la señal se realiza al reducir un nivel de potencia de la señal en una o más porciones de la señal en un dominio de frecuencia, filtrado espacial de la señal, selección de polarización de la señal o combinaciones de los mismos.
41
El medio de almacenamiento legible por máquina de conformidad con la reivindicación 35, caracterizado porque la señal cumple con un protocolo de interfaz común de radio pública (CPRI).
42
El medio de almacenamiento legible por máquina de conformidad con la reivindicación 35, caracterizado porque la señal se origina de antenas de múltiple entrada y múltiple salida (MIMO).
43
El medio de almacenamiento legible por máquina de conformidad con la reivindicación 35, caracterizado porque la detección comprende detectar la interferencia en la señal basándose en el conocimiento de que uno o más transmisores está causando la interferencia.

Independent claims43

306 paragraphs in 6 sections, as filed

METHOD AND APPLIANCE TO CONDUCT SIGNAL CONDITIONING TO MITIGATE THE INTERFERENCE DETECTED IN A COMMUNICATION SYSTEM

CROSS REFERENCE TO RELATED APPLICATIONS This application claims the priority benefit to U.S. Application No. 15 / 603,851 filed on May 24, 2017, which is incorporated herein by reference in its entirety.

This application also claims the priority benefit to U.S. Provisional Application No. 62 / 344,280 filed on June 1, 2016, which is hereby incorporated herein by reference in its entirety.

This application also claims the priority benefit to U.S. Provisional Application No. 62 / 481,789 filed on April 5, 2017, which is hereby incorporated herein by reference in its entirety.

FIELD OF DESCRIPTION

The object description relates to a method and an apparatus for increasing the performance of communication paths for communication nodes.

BACKGROUND OF THE DESCRIPTION

In most communication environments that involve short-range or long-range wireless communications, interference from unexpected wireless sources can affect the performance of a communication system, which leads to lower performance, dropped calls, bandwidth reduced that can cause traffic congestion or other adverse effects, which are undesirable.

Some wireless communication system service providers have addressed interference problems by adding more communication nodes, controlling interference or using antenna management techniques to avoid interference.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and where:

FIGURE 1 represents an illustrative embodiment of a communication system.

FIGURE 2 represents an illustrative embodiment of a frequency spectrum of a four-carrier CDMA signal.

FIGURE 3 depicts an illustrative embodiment of a frequency spectrum of a four-carrier CDMA signal that shows an unequal power balance between the four CDMA carriers and that includes an interference.

FIGURE 4 represents an illustrative embodiment of a base station of FIGURE

FIGURE 5 depicts an illustrative embodiment of a frequency spectrum of a four-carrier CDMA signal that has four CDMA carriers with suppression of an interference that results in falsification.

FIGURE 6 represents an illustrative embodiment of an interference detection and mitigation system.

FIGURE 7 represents an illustrative embodiment of an interference detection and mitigation system.

FIGURE 8 represents an illustrative embodiment of a signal processing module of FIGURE 7.

FIGURE 9 represents an illustrative mode of schemes of a propagated spectrum signal.

FIGURE 10 represents an illustrative embodiment of a method for interference detection.

FIGURE 11 represents illustrative modalities of the method of FIGURE 10.

FIGURE 12 represents illustrative modalities of a series of propagated spectrum signals interspersed with an interference signal.

FIGURE 13 represents an illustrative embodiment of a graph representing interference detection efficiency of a system of the object description.

FIGURE 14 depicts illustrative modalities of long-term Evolution (LTE) time and frequency signal schemes.

FIGURE 15 represents illustrative embodiments of LTE time and frequency signal schemes intermingled with interference signals.

FIGURE 16 represents an illustrative embodiment of a method for detecting and mitigating interference signals shown in FIGURE 15.

FIGURE 17 represents an illustrative mode of adaptive thresholds used to detect and mitigate interference signals shown in FIGURE 15.

FIGURE 18 represents an illustrative embodiment of signals resulting from LTE after mitigating interference according to the method of FIGURE 16.

FIGURE 19 represents an illustrative embodiment of a method for mitigating interference.

FIGURE 20 represents an illustrative embodiment of a network design.

FIGURE 21 represents an illustrative modality of an Open Systems Interconnection (OSI) model.

FIGURE 22 represents an illustrative modality of a relationship between SINR and data production and performance.

FIGURE 23 represents an illustrative embodiment of a closed loop process.

FIGURE 24 represents an illustrative embodiment of a spectral environment of a wireless channel.

FIGURE 25 represents an illustrative embodiment of examples of spectral environments for various frequency bands.

FIGURE 26A represents an illustrative embodiment of a method for managing links in a communication system.

FIGURE 26B represents an illustrative embodiment of a centralized system that manages cellular sites according to aspects of the object description.

FIGURE 26C represents an illustrative embodiment of cellular sites that operate independently in accordance with aspects of the object description.

FIGURE 26D represents an illustrative embodiment of cellular sites that cooperate with each other in accordance with aspects of the object description.

FIGURE 27A represents an illustrative embodiment of a method for determining an adaptive inter-cell interference threshold based on thermal noise measured from unused paths.

FIGURE 27B represents an illustrative embodiment of another method for determining an adaptive inter-cell interference threshold based on estimated thermal noise energy.

FIGURE 28 represents illustrative modalities of a system affected by passive intermodulation interference (PIM)

FIGURE 29 represents an illustrative embodiment of a system to mitigate PIM interference.

FIGURE 30A represents an illustrative embodiment of a system module of FIGURE 29.

FIGURE 30B represents an illustrative embodiment of a method used by the module of FIGURE 29.

FIGURE 31 represents an illustrative embodiment of a communication device that can be used entirely or in parts of the object description to detect and mitigate interference.

FIGURE 32 is a diagrammatic representation of a machine in the form of a computer system within which a set of instructions, when executed, may cause the machine to perform any of one or more of the methods described herein.

DETAILED DESCRIPTION OF THE INVENTION

The object description describes, among other things, illustrative modalities for detecting and mitigating interference signals. Other modalities are included in the object description.

One embodiment of the object description includes a system that has a memory for storing instructions and a processor coupled to the memory. Upon execution of the instructions by the processor, the processor may perform operations that include measuring the signals generated by the communication devices directed to the cellular site and measuring the noise levels for a plurality of paths. The processor can also perform operations that include measuring the interference signals for the plurality of paths according to the adaptive inter-cell interference threshold and, in turn, determining the Signal to Interference plus Noise (SINR) measurements for the plurality of paths according to the signals, noise levels and interference signals measured for the plurality of paths. The processor can also perform operations that include identifying a SINR measurement from SINR measurements that are below the SINR threshold and, in turn, initiate corrective action to improve the SINR measurement of an affected trajectory of the plurality of paths that fall below the SINR threshold.

One embodiment of the object description includes a machine-readable storage medium, which comprises instructions, which when executed by a processor, can cause the processor to perform operations that include obtaining a resource block program for each of a plurality of trajectories and, in turn, identify resource blocks in the plurality of paths that are not in use. The instructions may also cause the processor to perform operations that include measuring each of the plurality of paths and the energy of the resource blocks that are not in use to determine an average thermal noise level for each path and, at its instead, determine for each of the plurality of paths an adaptive inter-cell interference threshold according to the average thermal noise level of each path. The instructions may also cause the processor to perform operations that include measuring the interference signals according to the adaptive inter-cell interference threshold for each of the plurality of paths, measuring the signals and noise levels for each of the plurality of trajectories and, in turn, determine the measurements of Signal to Interference Ratio plus Noise (SINR) for the plurality of paths according to noise levels and interference signals measured for the plurality of paths. The instructions may cause the processor to perform operations that include identifying a SINR measurement that is below a SINR threshold and, in turn, initiating a corrective action to improve the SINR measurement of an affected trajectory of the plurality of trajectories that fall below the SINR threshold.

One embodiment of the object description includes a method, performed by a system comprising a processor, which includes obtaining performance measurements. Performance measurements may be determined from measurements associated with signals generated by communication devices, noise levels in a spectral portion used by communication devices to transmit the signals, and interference signals that exceed an adaptive interference threshold between cells. . The method may also include identifying a performance measurement from the performance measurements that are below the performance threshold and, in turn, initiate a corrective action to improve the performance measurement of an affected network element that falls below the performance threshold.

One modality of the object description includes a method for receiving, by means of a circuit, a signal generated by a communication device, which detects, by means of the circuit, an interference in the signal, the interference generated by one or more transmitters, and the Interference determined from signal characteristics associated with a signaling protocol used by one or more transmitters, wherein one or more transmitters are not associated with the communication device that generates the signal, and that performs, through the circuit, the conditioning of signals in the signal to reduce interference.

One embodiment of the object description includes a device that has an antenna and a circuit coupled to the antenna. The circuit may facilitate operations that include receiving, through the antenna, a signal generated by a communication device, detecting interference in the signal, interference generated by one or more transmitters, and interference determined from the associated signal characteristics with a signaling protocol used by one or more transmitters, where one or more transmitters are not associated with the communication device that generates the signal, and that performs signal conditioning on the signal to reduce interference.

One embodiment of the object description includes a machine-readable storage medium, comprising executable instructions that, when executed by a circuit, facilitate the performance of operations. Operations may include receiving, through an antenna, a signal generated by a communication device, detecting interference in the signal, interference generated by one or more transmitters, and interference determined from the signal characteristics associated with a protocol signaling used by one or more transmitters, where one or more transmitters are not associated with the communication device that generates the signal, and that performs signal conditioning on the signal to reduce interference.

As shown in FIGURE 1, an exemplary telecommunications system 10 can include mobile units 12,13A, 13B, 13C and 13D, a number of base stations, of which two are shown in FIGURE 1 with reference numbers 14 and 16, and a switching station 18 to which each of the base stations 14, 16 can be interconnected. The base stations 14, 16 and the switching station 18 can be collectively referred to as network infrastructure.

During operation, mobile units 12,13A, 13B, 13C and 13D exchange voice, data or other information with one of the base stations 14, 16, of which each is connected to a conventional land line communication network. For example, information, such as voice information, transferred from the mobile unit 12 to one of the base stations 14, 16 is coupled from the base station to the communication network to thereby connect the mobile unit 12 with, by For example, a landline phone line so that the landline phone can receive voice information. Conversely, information, such as voice information, can be transferred from a landline communication network to one of the base stations 14, 16, which in turn transfers the information to the mobile unit 12.

Mobile units 12, 13A, 13B, 13C and 13D and base stations 14, 16 can exchange information in narrowband or broadband format. For the purposes of this description, it is assumed that the mobile unit 12 is a narrowband unit and that the mobile units 13A, 13B, 13C and 13D are broadband units. Additionally, it is assumed that the base station 14 is a narrowband base station that communicates with the mobile unit 12 and that the base station 16 is a digital broadband base station that communicates with the mobile units 13A, 13B, 13C and 13D.

Communication in narrow band format is performed using, for example, narrow band channels of 200 kllohertz (KHz). The Global system for mobile telephone systems (GSM) is an example of a narrowband communication system in which the mobile unit 12 communicates with the base station 14 using narrowband channels. Alternatively, mobile units 13A, 13B, 13C and 13D communicate with base stations 16 using a form of digital communications such as, for example, code division multiple access (CDMA), Universal Mobile Telecommunication System (UMTS), Long Term Evolution (LTE) of 3GPP, or other next-generation wireless access technologies. Digital communication of CDMA, for example, is done using propagated spectrum techniques that broadcast signals with broad bandwidths, such as, for example, bandwidths of 1,288 megahertz (MHz).

The switching station 18 is generally responsible for coordinating the activities of the base stations 14, 16 to ensure that the mobile units 12, 13A, 13B, 13C and 13D are in constant communication with the base station 14, 16 or with some other base stations that are geographically dispersed. For example, the switching station 18 can coordinate communication transfers of the mobile unit 12 between the base stations 14 and another base station as the mobile unit 12 makes interconnection between geographic areas that are covered by the two base stations.

A particular problem that may arise in the telecommunication system 10 is when the mobile unit 12 or the base station 14, of which each communicates using narrowband channels, interferes with the ability of the base station 16 to receive and process Digital broadband signals from digital mobile units 13A, 13B, 13C and 13D. In such a situation, the narrowband signal transmitted from the mobile unit 12 or the base station 14 may interfere with the ability of the base station 16 to adequately receive broadband communication signals.

As will be readily appreciated, the base station 16 can receive and process digital broadband signals from more than one of the digital mobile units 13A, 13B, 13C and 13D. For example, the base station 16 can be adapted to receive and process four CDMA carriers 40A-40D that fall within a multi-carrier CDMA signal 40, as shown in FIGURE 2. In such a situation, narrowband signals are transmitted from more than one of the mobile units, such as mobile unit 12, may interfere with the ability of the base station 16 to properly receive broadband communication signals in any of the four carriers of CDMA 40A-40D. For example, FIGURE 3 shows a multi-carrier CDMA signal 42 containing four CDMA carriers 42A, 42B, 42C and 42D adjacent to each other, wherein one of the CDMA carriers 42C has a narrowband interference 46 therein. . As shown in FIGURE 3, it is quite common that the signal strengths of the CDMA carrier signals 42A-42D are not the same.

As described in detail below, a system and / or a method for adaptive multi-channel filtering or interference suppression can be used in a communication system. In particular, such a system or method can be used in a communication system to protect against or to report the presence of interference, which has detrimental effects on the performance of the communication system. In addition, such a system and method can be operated to eliminate interference in CDMA carriers that have other CDMA carriers adjacent thereto.

The above system and methods can also be applied to other protocols such as AMPS, GSM, UMTS, LTE, VoLTE, 802.11xx, 5G, next-generation wireless protocols, etc. Additionally, the terms narrowband and broadband mentioned in the foregoing may be replaced with subbands, concatenated bands, bands between carrier frequencies (carrier addition), etc., without departing from the scope of the object description. In addition, it should be noted that the term interference may represent emissions within the band (narrow band or broadband), out-of-band interference, sources of out-of-band interference (e.g. TV stations, commercial radio or public safety radio) , interference signals from other carriers (interference between carriers), interference signals from user equipment (UEs) operating in adjacent base stations, and so on. The interference may represent any foreign signal that may affect communications between communication devices (for example, a UE served by a particular base station).

As shown in FIGURE 4, the signal reception path of the base station 16, which was described as receiving interference from the mobile unit 12 together with FIGURE 1, includes an antenna 50 that provides signals to an amplifier 52 . The output of the amplifier 52 is coupled to a diplexer 54 that divides the signal from the amplifier 52 into a series of different paths, of which one may be coupled to an adaptable input terminal 56 and of which another may be coupled to a receiver A 58. The output of the adaptive input terminal 56 is coupled to a receiver B 59, which may, for example, be represented on a CDMA receiver or any other suitable receiver B. Although only one signal path is shown in Figure 4, those of ordinary skill in the art will readily understand that such a signal path is merely exemplary and that, in reality, a base station may include two or more signal paths that can be used. to process main and diversity signals received by the base station 16.

It will be readily understood that the illustrations of FIGURE 4 can also be used to describe the components and functions of other forms of communication devices such as a small cell base station, a microcell base station, a picocell base station, a femtocell, a WiFi router or access point, a cell phone, a smartphone, a laptop, a tablet or other forms of wireless communication devices suitable for applying the principles of the object description. Accordingly, such communication devices may include variants of the components shown in FIGURE 4 and perform the functions described below. For illustrative purposes only, the following descriptions will address base station 16 with the understanding that these modalities are exemplary and not limiting the object description.

With reference again to FIGURE 4, the outputs of receiver A 58 and receiver B 59 can be coupled to other systems within the base station 16. Such systems can perform voice and / or data processing, call processing or any Another desired function. Additionally, the adaptive input terminal module 56 can also be communicatively coupled, via the Internet, telephone lines, cellular networks or any other suitable communication systems, to a control and reporting facility that is remote from the base station 16 In some networks, the control and reporting facility may be integrated with switching station 18. The receiver A 58 can be communicatively coupled to the switching station 18 and can respond to the commands issued by the switching station 18.

Each of the components 50-60 of the base station 16 shown in FIGURE 4, with the exception of the adaptable input terminal module 56, can be found in a conventional cellular base station 16, the details of which are well known to those of ordinary experience in the technique Someone of ordinary skill in the art will also appreciate that FIGURE 4 does not describe all the systems or subsystems of the base station 16 and, rather, focuses on the systems and subsystems relevant to the object description. In particular, it will be readily appreciated that, although not shown in FIGURE 4, the base station 16 may include a transmission system or other subsystems. Furthermore, it is appreciated that the adaptive input terminal module 56 may be an integral subsystem of a cellular base station 16, or it may be a modular subsystem that can be physically placed in different locations of a receiver chain of the base station 16, such such as at or near antenna 50, at or near amplifier 52, or at or near receiver B 59.

During the operation of the base station 16, the antenna 50 receives CDMA carrier signals that are broadcast from the mobile unit 13A, 13B, 13C and 13D and couples such signals to the amplifier 52, which amplifies the received signals and couples the amplified signals to the diplexer 54. Diplexer 54 divides the amplified signal from amplifier 52 and essentially places copies of the amplified signal on each of its output lines. The adaptive input terminal module 56 receives the diplexer 54 signal and, if necessary, filters the CDMA carrier signal to eliminate any unwanted interference and couples the filtered CDMA carrier signal to the B 59 receiver.

As noted above, FIGURE 2 illustrates an ideal frequency spectrum 40 of a CDMA carrier signal that can be received on antenna 50, amplified and divided by amplifier 52 and diplexer 54 and coupled to the terminal module Adaptable input 56. If the CDMA carrier signal received on the antenna 50 has a frequency spectrum 40 as shown in FIGURE 2 without any interference, the adaptive input terminal will not filter the CDMA carrier signal and will simply couple the signal directly through from the adaptive input terminal module 56 to receiver B 59.

However, as noted above, it is possible that the CDMA carrier signal transmitted by mobile units 13A-13D and received by antenna 50 has a frequency spectrum as shown in FIGURE 3 that contains a signal from Multi-carrier CDMA 42 that includes not only the four CDMA carriers 42A, 42B, 42C and 42D of the mobile units 13A, 13B, 13C and 13D that have uneven CDMA carrier resistors, but also include the interfering 46, as shown in FIGURE 3, which in this illustration is caused by the mobile unit 12. If the antenna 50 receives a multi-carrier CDMA signal that has a multi-carrier CDMA signal 42 that includes an interfering 46 and amplifies it, it divides and featuring the adaptive input terminal module 56, it will filter the multi-carrier CDMA signal 42 to produce a filtered frequency spectrum 43 as shown in FIGURE 5.

The filtered multi-carrier CDMA signal 43 has the interference 46 removed, as shown by the notch 46A. The filtered multi-carrier CDMA signal 43 is then coupled from the adaptive input terminal module 56 to the B 59 receiver, so that the filtered multi-carrier CDMA signal 43 can be demodulated. Although part of the multi-carrier CDMA signal 42 was removed during filtering by the adaptive input terminal module 56, sufficient multi-carrier CDMA signal 43 is left to allow receiver B 59 to retrieve the information transmitted by the mobile unit (s) . Therefore, in general terms, the adaptive input terminal module 56 selectively filters the multi-carrier CDMA signals to remove the interference from it. Additional details regarding the adaptive input terminal module 56 and its operation are provided below together with FIGURE 6, FIGURE 7, FIGURE 8, FIGURE 9, FIGURE 10, FIGURE 11, FIGURE 12, FIGURE 13, FIGURE 14, FIGURE 15, FIGURE 16, FIGURE 17, FIGURE 18, FIGURE 19, FIGURE 20 AND FIGURE 21.

FIGURE 6 represents another exemplary embodiment of the adaptive input terminal module 56. As noted above, the adaptive input terminal module 56 can be used by any communication device, including cell phones, smartphones, tablets, stations Small base, femtocells, WIFi access points, etc. In the illustration of FIGURE 3, the adaptive input terminal module 56 may include a radio 60 comprising two phases, a receiver phase 62 and a transmitter phase 64, each coupled to an antenna assembly 66, 66 ' , which may comprise one or more antennas for radio 60. Radio 60 has a first receiver phase coupled to antenna assembly 66 and includes an adaptive input terminal controller 68 that receives the input RF signal from the antenna and performs adaptive signal processing on that RF signal before providing the modified RF signal to an analog to digital signal converter 70, which then passes the adapted RF signal to a digital RF tuner 72.

As shown in FIGURE 6, the adaptive input terminal controller 68 of the receiver phase 62 includes two RF signal samplers 74, 76 connected between an adaptive RF filter phase 78 that is controlled by the controller 80. The adaptive filter phase 78 may have a plurality of tuning digital filters that can sample an incoming signal and selectively provide the conformation of band pass signal or band suppression of an incoming RF signal, either a complete communication signal or a subband signal or several combinations of both. A controller 80 is coupled to the samplers 74, 76 and the filtering phase 78 and serves as an RF link adapter that, together with the sampler 74, controls the RF input signal of the antenna 66 and determines various characteristics of RF signal, such as interference and noise within the RF signal. The controller 80 is configured to execute any number of a variety of signal processing algorithms to analyze the received RF signal and determine a filter state for the filtering phase 78.

By providing tuning coefficient data to the filtering phase 78, the adaptive input terminal controller 68 acts to pre-filter the received RF signal before the signal is sent to the RF tuner 72, which analyzes the signal of filtered RF to determine integrity and / or for other applications such as cognitive radio applications. After filtering, the radio tuner 72 can then perform channel demodulation, data analysis and local broadcast functions. The RF tuner 72 can be considered the receiver side of a general radio tuner, while the RF tuner 72 'can be considered the transmitter side of the same radio tuner. Before sending the sign! of filtered RF, the sampler 76 can provide an indication of the filtered RF signal to the controller 80 in a feedback manner for further adjustment of the adaptive filter phase 78.

In some examples, the adaptive input terminal controller 68 synchronizes with the RF tuner 72 when sharing a master clock signal communicated between the two. For example, cognitive radios operating in a response time of 100 ps can be synchronized so that, for each clock cycle, the adaptive input terminal analyzes the input RF signal, determines an optimal configuration for the filter phase adaptable 78, filters that RF signal into the filtered RF signal and communicates it to radio tuner 72 for cognitive analysis on the radio. As an example, cell phones can be implemented with a response time of 200 ps in the filtering. By implementing the adaptive input terminal controller 68 using a field programmable gate layout configuration for the filter phase, wireless devices can identify not only stationary interference, but also non-stationary interference, of arbitrary bandwidths in That interfering on the move.

In some implementations, the adaptive input terminal controller 68 may filter the interference or noise of the incoming RF signal received and pass that filtered RF signal to the tuner 72. In other examples, such as cascade configurations in which there are multiple adaptive filter phases, the adaptive input terminal controller 68 can be configured to apply the filtered signal to an adaptive bandpass filter phase to create a portion of Pass band of the filtered RF signal. For example, the radio tuner 72 may communicate information to the controller 80 to indicate to the controller that the radio is only looking at a portion of a general RF spectrum and, therefore, cause the adaptive input terminal controller not to filter certain portions of the RF spectrum and therefore the band passes only those portions The integration between the radio tuner 72 and the adaptive input terminal controller 68 may be particularly useful in applications Dual band and triple band in which the radio tuner 72 can communicate over different wireless standards, such as GSM, UMTS or LTE standards.

The algorithms that can be executed by controller 80 are not limited to interference detection and interference signal filtering. In some configurations, controller 80 may execute a spectral blind source separation algorithm that seeks to isolate two sources from its convoluted mixtures. The controller 80 can execute a signal to interference noise ratio (SINR) output estimator for all or portions of the RF signal. The controller 80 can perform bi-directional transceiver data link operations for collaborative resetting of the adaptive filter phase 78 in response to the instructions of the radio tuner 72 or the data of the transmitter phase 64. The controller 80 can determine the filter adjustment coefficient data to configure the various adaptable filters of phase 78 to adequately filter the RF signal. The controller 80 may also include a data interface that communicates the tuning coefficient data to the radio tuner 72 to allow the radio tuner 72 to determine the filtering characteristics of the adaptive filter 78.

In one embodiment, the filtered RF signal can be converted from a digital signal to an analog signal within the adaptive input terminal controller 68. This allows the controller 80 to be integrated in a manner similar to conventional RF filters. In other examples, a digital interface can be used to connect the adaptive input terminal controller 68 with the radio tuner 72, in which case the ADC 70 may not be necessary.

The above discussion is in the context of the receiver phase 62. Similar elements are shown in the transmitter phase 64, but bearing a premium. The elements in the transmitter phase 64 may be similar to those of the receiver 62, with the exception of the digital to analog converter (DAC) 70 'and other adaptations to the other components shown with a premium in the reference numbers. In addition, some or all of these components may, in fact, be executed by the same corresponding structure in the receiver phase 62. For example, the RF receiver tuner 72 and the transmitter tuner 72 'can be performed by a single device tuner. The same may be true for the other elements, such as the adaptive filter phases 78 and 78 ', which can be implemented in a single FPGA, with different filter elements in parallel for the operation of reception and transmission completely duplex (simultaneous).

FIGURE 7 illustrates another exemplary implementation of an adaptive input terminal controller 100. The input RF signals are received on an antenna (not shown) and are coupled to an initial analog filter 104, such as an amplifier block of Low noise (LNA), then converted digitally by an analog to digital converter (ADC) 106, before the digitized input RF signal is coupled to a programmable field gate (FPGA) arrangement 108. The adaptive filter phase described above can be implemented within the FPGA 108, which has been programmed to contain a plurality of adaptable filter elements tunable at different operating frequencies and frequency bands, and at least some are adaptable from one step band to a band suppression setting or vice versa, as desired. Although an FPGA is illustrated, it will be readily understood that other architectures, such as a specific application integrated circuit (ASIO) or a digital signal processor (DSP), can also be used to implement a digital filter architecture described in greater detail to continuation.

A DSP 110 is coupled to the FPGA 108 and executes signal processing algorithms that may include a spectral blind source separation algorithm, a signal to interference noise ratio output estimator (SINR), data line operation of Bidirectional transceiver for collaborative readjustment of the adaptive filter phase in response to tuner instructions, and / or an optimal algorithm of filter adjustment coefficients.

The FPGA 108 is also coupled to a PCI 112 lens that interconnects with the FPGA 108 and a PCI 114 bus for external data communication. A system clock 118 provides a clock input to the FPGA 108 and the DSP 110, thereby synchronizing the components. The system clock 118 can be configured locally on the adaptive input terminal controller, while, in other examples, the system claim 118 may reflect an external master clock, such as that of a radio tuner. FPGA 108, DSP 110 and PCI 112 objective, collectively designated as signal processing module 116, will be described in more detail below. In the example illustrated, the adaptive input terminal controller 100 includes a microcontroller 120 coupled to the PCI 114 bus and an operation, alarm and metric (OA&M) processor 122.

Although they are shown and described herein as separate devices that execute separate software instructions, those of ordinary skill in the art will readily appreciate that the functionality of microcontroller 120 and OA&M processor 122 can be combined into a single processing device. The microcontroller 120 and the OA&M processor 122 are coupled to the external memories 124 and 126, respectively. Microcontroller 120 may include the ability to communicate with peripheral devices and, as such, microcontroller 120 may be coupled to a USB port, an Ethernet port or an RS232 port, among others (although none are shown). In operation, microcontroller 120 can store locally lists of interfering channels or a list of known frequency spectrum bands typically available, as well as various other parameters. Such a list can be transferred to a reporting and control facility or a base station, using OA&M processor 122, and can be used for system diagnostic purposes.

The diagnostic purposes mentioned in the foregoing may include, but are not limited to, controlling the adaptive input terminal controller 100 to obtain particular information related to an interferer and reassigning a task to the interferer. For example, the reporting and control facility may use the adaptive user interface controller 100 to determine the identity of an interference agent, such as a mobile unit, when intercepting the electronic serial number (ESN) of the mobile unit, which is sent when the mobile unit transmits information on the channel. Upon knowing the identity of the interference party, the reporting and control facility may contact the infrastructure that communicates with the mobile unit (for example, the base station) and may request that the infrastructure change the transmission frequency for the mobile unit (i.e. the frequency of the channel on which the mobile unit transmits) or can request the infrastructure to abandon communications with the interfering mobile unit completely.

Additionally, in a cellular configuration (for example, a system based on a configuration such as that of FIGURE 1), diagnostic purposes may include the use of the adaptive input terminal controller 100 to determine a telephone number that the mobile unit attempts to Contact and optionally handle the call. For example, the reporting and control service may use the adaptive input terminal controller 100 to determine that the user of the mobile unit dialed 911, or any other emergency number, and, therefore, may decide that The adaptive input terminal controller 100 must be used to handle the emergency call when routing the output of the adaptive input terminal controller 100 to a telephone network.

The FPGA 108 may provide a digital output coupled to a digital to analog converter (DAC) 128 that converts the digital signal into an analog signal that can be provided to a filter 130 to generate a filtered RF output to be transmitted from the base station or mobile station. The digital output on the FPGA 108, as described, may be one of the many possible outputs. For example, FPGA 108 can be configured to produce signals based on a predefined protocol such as a Gigabit Ethernet output, an open base station architecture initiative (OBSAI) protocol or a common public radio interface (CPRI) protocol, among others.

In addition, it should be noted that the diagnostic purposes mentioned in the above may also include the creation of a database of known interferers, the time of occurrence of the interferers, the frequency of occurrence of the interferers, the spectral information that relates to the interferers, an analysis of the severity of the interferers, etc. The identity of the interferers may be based solely on the spectral profiles of each interferent that can be used for identification purposes. Although the aforementioned illustrations describe a mobile unit 12 as interfering, other sources of interference are possible. Any electronic device that generates electromagnetic waves such as a computer, an encoder box, a child monitor, a wireless access point (for example, WiFi, ZigBee, Bluetooth, etc.) can be a source of interference. In one embodiment, a database of electronic devices in a laboratory environment or other suitable test environment can be analyzed to determine an interference profile for each device. The interference profiles can be stored in a database according to the type of device, the manufacturer, the model number and other parameters that may be useful for identifying an interference. The spectral profiles provided, for example, by the OA&M 108 processor to a diagnostic system can be compared with a database of interferers previously characterized to determine the identity of the interference when a match is detected.

A diagnostic system, whether operating locally on the adaptive input terminal controller, or remotely at a base station, switching station or server system, can determine the location of the interference near the base station (or unit mobile) that performs the detection, or if a more precise location is required, The diagnostic system can tell several base stations (or mobile units) to perform a triangulation analysis to more accurately locate the source of the interference if the interference is frequent and measurable from several points of view. With the location data, the identity of the interference, the time and the frequency of occurrence, the diagnostic system can generate temporary and geographical reports that show the interferers, which provides the field staff with a means to assess the volume of interference, its impact on network performance, and can provide sufficient information to mitigate interference by means other than filtering, such as avoid interference by means of the direction of the antennas at the base station, the direction of beams, re-assignment of an interference's tasks whenever possible, etc.

FIGURE 8 illustrates additional details of an exemplary implementation of a signal processing module 116 that can serve as another modality of an adaptive input terminal controller, it being understood that other architectures can be used to implement a signal detection algorithm. A decoder 150 receives an input from ADC 106 and decodes the incoming data in a format suitable for processing by signal processing module 116. A digital down converter 152, such as a multiphase decimator, converts the decoded signal from the decoder 150. The decoded signal is separated during the digital down conversion stage into a complex representation of the input signal, that is, into the components En- Phase (I) and Quadrature-Phase (Q) which are then introduced into a tunable infinite impulse response filter (IIR) Finite impulse response (FIR) 154. The IIR / FIR filter 154 can be implemented as multiple 11R and FIR filters in cascade or parallel. For example, the IIR / FIR filter 154 can be used with multiple filters in series, such as the initial adaptive bandpass filter followed by the adaptive band suppression filter. For example, bandpass filters can be implemented as FIR filters, while band suppression filters can be implemented as IIR filters. In one embodiment, fifteen cascade adjustable IIR / FIR filters are used to optimize the bit width of each filter. Of course, other converters and digital filters, such as cascade integration comb (CIC) filters, can be used, to name a few. By using complex filtering techniques, such as the technique described herein, the sampling rate is reduced, thereby increasing (for example, doubling) the bandwidth that the filter 154 can handle. In addition, using complex arithmetic also provides the signal processing module 116 with the ability to perform higher filtering orders with greater precision.

The components I and Q of the digital down converter 152 are supplied to the DSP 110, which implements a detection algorithm and, in response, provides the tunable IIR / FIR filter 154 with tuning coefficient data that tune the IIR filters and / or FIR 154 at a specific notch (or band suppression) and / or bandpass frequencies, respectively, and specific bandwidths. The tuning coefficient data, for example, can include a frequency and a pair of bandwidth coefficients for each of the adaptable filters, which allows the filter to tune a frequency for the band pass or the suppression operation. of bandwidth and the bandwidth that will be applied for that operation. The detection algorithm can generate the tuning coefficient data corresponding to the central bandwidth frequency and bandwidth and pass them to a tunable FIR filter within the IIR / FIR filter 154. Filter 154 can then pass all signals located within a band of the given transmission frequency. The tuning coefficient data corresponding to a notch filter (or band suppression) can be generated by the detection algorithm and then applied to an IIR filter within the IIR / FIR filter 154 to eliminate any interference located within the pass band of the band pass filter. The tuning coefficient data generated by the detection algorithm is implemented by the tunable IIR / FIR 154 filters using mathematical techniques known in the art. In the case of a cognitive radio, after the implementation of the detection algorithm, the DSP 110 can determine and return the coefficients that correspond to a specific frequency and bandwidth to be implemented by the tunable IIR / FIR 154 filter through a DSP / PCI 158 interface. Similarly, the transfer function of a notch filter (or band suppression) can also be implemented by the IIR / FIR tunable filter 154. Of course, other mathematical equations can be used to tune the IIR / FIR 154 filters at specific notch, bandwidth or bandpass frequencies and at a specific bandwidth.

After components I and Q are filtered at the appropriate notch frequency (or band suppression) or bandpass at a given bandwidth, an ascending digital converter 156, such as a multi-phase interpolator, converts the signal again at the original data rate, and the output of the digital up converter is provided to DAC 128.

A wireless communication device capable of operating as a double or triple band device that communicates through multiple standards, such as, for example, UMTS and LTE, can use the adaptive digital filter architecture modalities as described herein. previous. For example, a dual band device (which uses LTE and UMTS) can be pre-programmed within DSP 110 to first transmit on LTE, if available, and on UMTS only when it is outside of an LTE network. In such a case, the IIR / FIR filter 154 can receive tuning coefficient data from the DSP 110 to pass all signals within the range of LTE. That is, the tuning coefficient data may correspond to a central bandwidth frequency and a bandwidth adapted to pass only signals within the range of LTE. The signals corresponding to a UMTS signal can be filtered, and any interference caused by the UMTS signal can be filtered using tuning coefficients, received from the DSP 110, which correspond to a notch frequency (or band suppression) and bandwidth. band associated with the UMTS interference signal.

Alternatively, in some cases, it may be desirable to maintain the UMTS signal in case the LTE signal fades quickly and the wireless communication device must change the communication standards quickly. In such a case, the UMTS signal can be separated from the LTE signal, and both are passed through the adaptive input terminal controller. Using the adaptive digital filter, two outputs can be made, an output corresponding to the LTE signal and an output corresponding to a UMTS signal. The DSP 110 can be programmed to recognize the multiple standard service again and can generate tuning coefficients that correspond to performing a filter, such as a notch filter (or band suppression), to separate the LTE signal from the UMTS signal. . In such examples, an FPGA can be programmed to have parallel adaptive filter phases, one for each communication band.

To implement the adaptive filter phases, in some examples, the signal processing module 116 is pre-programmed with a general filter architecture code at the time of production, for example, with parameters defining various types of filter and operation. . The adaptive filter phases can be programmed, through a user interface or other means, by service providers, device manufacturers, etc., to form the actual filter architecture (parallel filter phases, filter phases in cascade, etc.) for the particular device and for the particular network (s) in which the device will be used. Dynamic flexibility can be achieved during runtime, where filters can be programmed for different frequencies and bandwidths, each cycle, as discussed herein.

One method of detecting a signal that has interference is to take advantage of the noise-like characteristics of a signal. Due to the noise-like characteristics of the signal, a particular measurement of the channel power does not provide a predictive power as to what the next measurement of the same measurement channel may be. In other words, consecutive observations of power in a given channel are not correlated. As a result, if a given measurement of power in a channel provides predictive power over subsequent power measurements in that particular channel, thus indicating a deviation from the expected statistics of a channel without interference, it may be determined that such a channel contains interference.

FIGURE 9 illustrates an IS-95 202 CDMA signal, which is a generic Direct Sequence Propagation Spectrum (DSSS) signal. The CDMA 202 signal can have a bandwidth of 1.2288 MHz and can be used to carry up to 41 channels, each of which has a bandwidth of 30 kHz. One way to identify the interference that affects the CDMA signal 202 may be to identify any of the 41 channels that have an excess power above an expected power of the CDMA signal 202.

FIGURE 9 also illustrates the probability distribution functions (PDFs) 204 of a typical DSSS signal and complementary cumulative distribution functions (CCDFs) 206 of a typical DSSS signal, which can be used to establish a criterion used to determine the channels arranged within a signal and that have excess power.

Specifically, PDFs 204 include the distribution of power probability in a given channel, which is the probability p (x) of measuring a power x in a given channel, for a DSSS signal carrying a mobile unit (212), for a DSSS signal carrying ten mobile units (214), and for a DSSS signal carrying twenty mobile units (210). For example, for PDF 212, which represents a DSSS signal carried by a mobile unit, it is observed that the distribution p (x) is asymmetric, with a high power queue abbreviated. In this case, any channel having a power greater than the high power queue of PDF 212 can be considered to have an interference signal.

The CCDFs 206 indicate the probability that a power measurement in a channel will exceed a given average power to, by the way, wing value, where σ is the standard deviation of the power distribution. Specifically, CCDFs 206 include a CCDF instance for a DSSS signal carrying a mobile unit (220), a CCDF instance for a DSSS signal carrying ten mobile units (222), and a CCDF instance for a signal DSSS carrying twenty mobile units (224). Thus, for example, for a DSSS signal carried by a mobile unit, the probability that a certain channel has a wing ratio of 10 dB or more is 0.01%. Therefore, an optimal filter can be tuned for a channel that has excess power.

One method of detecting a channel that has interference is to take advantage of the noise-like characteristic of a DSSS signal. Due to such a noise-like characteristic of the DSSS signal, a particular measurement of a channel power does not provide predictive power as to what the next measurement of the same measurement channel may be. In other words, consecutive power observations on given channels are not correlated. As a result, if a given measurement of power in a channel provides predictive power over subsequent power measurements in that particular channel, thus indicating a deviation from the expected statistics of a channel without interference, it may be determined that such a channel contains interference.

FIGURE 10 illustrates a flow chart of an interference detection program 300 that can be used to determine the location of interference in a DSSS signal. In block 302, the adaptive input terminal controller described above can take advantage of a series of DSSS signals and the observed values of the signal resistors can be stored for each of the different channels located in the DSSS signal. For example, in block 302, the adaptive input terminal controller can continuously scan the 1.2288 MHz DSSS 60 signal for each of the 41 channels dispersed therein. The adaptive input terminal controller can be implemented by any known analog scanner or digital signal processor (DSP) that is used to scan and store the signal resistors in a DSSS signal. The scanned values of the signal resistors can be stored in a memory of such a DSP or in any other computer readable memory. The adaptive input terminal controller can store the signal resistance of a particular channel along with any information, such as a numerical identifier, that identifies the location of that particular anal c within the DSSS signal.

In block 304, the adaptive input terminal controller can determine the number of sequences m of a DSSS signal to be analyzed to determine the channels that have interference. A user can provide a number m based on any predetermined criteria. For example, a user can provide m to be equal to four, which means that it is necessary to analyze four consecutive DSSS signals to determine if any of the channels within that DSSS signal spectrum includes an interference signal. As someone with ordinary experience in the art can appreciate, the higher the selected value of m, the more accurate the interference detection will be. However, the higher the number m, the longer the delay will be to determine if a particular DSSS signal had an interference present, subsequently, resulting in a longer delay before a filter is applied to the DSSS signal for Eliminate the interference signal.

Generally, the detection of an interference signal can be performed on a continuous basis. That is, at any point in time, m previous DSSS signals can be used to analyze the presence of an interference signal. The first of such m interference signals can be removed from the set of DSSS signals used to determine the presence of an interference signal on a first-in, first-out basis. However, in an alternative mode, an alternative sampling method can also be used for the set of DSSS signals.

In block 306, the adaptive input terminal controller can select x channels that have the highest signal resistance of each of the most recent DSSS signals scanned in block 302. The user can determine the number x. For example, if x is selected to equal three, block 306 can select three higher channels from each of the most recent m DSSS signals. The methodology for selecting x channels that have the highest signal resistance of a DSSS signal is described further detail in FIGURE 11 below. For example, the adaptive input terminal controller in block 306 may determine that the first of the m signals of

DSSS have channels 10, 15 and 27 with the highest signal strengths, the second of the m DSSS channels has channels 15 and 27 and 35 with the highest signal strengths and the third of the m DSSS channels has the channels 15, 27 and 35 that have the highest signal resistance.

After having determined the x channels that have the highest signal strengths in each of the m DSSS signals, in block 308, the adaptive input terminal controller can compare these x channels to determine if any of these channels of Higher resistance appears more than once in the m DSSS signals. In the case of the previous example, the adaptive input terminal controller in block 308 can determine that channels 15 and 27 are presented among the highest resistance channels for each of the last three DSSS signals, while channel 35 It is presented among the highest resistance channels for at least two of the last three DSSS signals.

Such a consistent appearance of channels that have the highest signal resistance compared to subsequent DSSS signals indicates that channels 15 and 27, and probably channel 35, may have an interference signal superimposed on them. In block 310, the adaptive input terminal controller may use such information to determine which channels may have interference. For example, based on the number of times a given channel appears on the highest selected signal resistance channels, the adaptive input terminal controller in block 310 can determine the level of confidence that can be assigned to a conclusion that A given channel contains an interference signal.

Alternatively, in block 310, the adaptive input terminal controller can determine a correlation factor for each of the various channels that appear on the x channels of highest signal strength selected and compare the calculated correlation factors with a factor of threshold correlation to determine if any of the x channels selected have correlated signal strengths. Calculating a correlation factor based on a series of observations is well known to those of ordinary skill in the art and, therefore, is not illustrated in greater detail herein. The user of the interference detection program 300 can provide the threshold correlation factor.

Note that, although in the mode illustrated above, the correlation factors of only the channels with the highest signal strength selected are calculated, in an alternative mode, the correlation factors of all channels within the channel can be calculated and compared. DSSS signals with the threshold correlation factor.

Empirically, it can be shown that when m is selected to equal three, for a clean DSSS signal, the probability of having at least one match between the highest signal resistance channels is 0.198, the probability of having at least two matches between the channels with the highest signal resistance is 0.0106, and the probability of having at least three matches between the channels with the highest signal resistance is 9.38 x 10-5. In this way, the higher the number of matches, the lower the probability of having a determination that one of the x channels contains an interference signal (i.e., a false positive interference detection). It can be shown that, if the number of m scans increases, say four DSSS scans, the probability of having such matches in m consecutive scans is even lower, which provides greater confidence that if such matches are found to exist, they indicate the presence of an interference signal on those channels.

To identify the presence of interference signals with an even higher level of confidence, in block 312, the adaptive input terminal controller can decide whether to compare the signal resistances of the channels determined to have an interference signal with a threshold. . If in block 312 the adaptive input terminal controller decides to make such a comparison, in block 314 the adaptive input terminal controller can compare the signal resistance of each of the channels that is determined to have an interference with a level threshold Such a comparison of the channel signal resistors with a threshold can provide greater confidence with respect to the channel having an interference signal, so that when a filter is configured according to the channel, the probability of eliminating a signal is reduced not interfering However, a user can determine that such a level of added trust is not necessary and, thus, it is not necessary to make such a comparison with a threshold. In which case, in block 316, the adaptive input terminal controller stores the interference signals in a memory.

After storing the information on the channels that have interference signals, in block 318, the adaptive input terminal controller selects the next DSSS signal from the signals scanned and stored in block 302. In block 318, the adaptive input terminal controller may cause the first of the m DSSS signals to drop and the newly added DSSS signal is added to the set of m DSSS signals that will be used to determine the presence of a interference signal (first in, first out). Subsequently, in block 306, the process of determining channels having interference signals is repeated by the adaptive input terminal controller. Finally, in block 320, the adaptive input terminal controller can select and activate one or more filters that are located in the DSSS signal path to filter any channel identified as having interference therein.

Referring to FIGURE 11, a flowchart illustrates a high resistance channel detection program 350 that can be used to identify several channels within a given scan of the DSSS signal that may contain an interference signal. The high resistance channel detection program 350 can be used to implement the functions performed in block 306 of the interference detection program 300. In a manner similar to the interference detection program 300, the high resistance channel detection program 350 can also be implemented using software, hardware, firmware or any combination thereof.

In block 352, the adaptive input terminal controller can classify the signal resistors of each of the n channels within a given DSSS signal. For example, if a DSSS signal has 41 channels, in block 352, the adaptive input terminal controller can classify each of the 41 channels according to their signal strengths. Subsequently, in block 354, the adaptive input terminal controller can select the x highest resistance channels of the classified channels and store information that identifies the x highest resistance channels selected for further processing. One mode of the high resistance channel detection program 350 can simply use the x highest resistance channels selected from each scan of the DSSS signals to determine any interference presence in the DSSS signals. However, in an alternative mode, additional selected criteria may be used.

Subsequently, in block 356, the adaptive input terminal controller can determine whether it is necessary to compare the signal resistors of the x channels of highest resistance with any other signal resistance value, such as a threshold signal resistance, etc. ., where such threshold can be determined using the average signal resistance across the DSSS signal. For example, in block 356, the adaptive input terminal controller may use a criterion such as, for example: "When x is selected to be four, if at least three out of four of the selected channels have also appeared in signals from previous DSSS, no additional comparison is necessary ”. Another criterion may be, for example: “if any of the selected channels is in the DSSS signal range, the signal strengths of such channels must be compared with a threshold signal resistance. Other alternative criteria may also be provided.

If in block 356 the adaptive input terminal controller determines that no additional comparison of the signal strengths of the selected x channels is necessary, in block 358 the adaptive input terminal controller stores information about the selected x channels in A memory for further processing. If in block 356 the adaptive input terminal controller determines that it is necessary to apply additional selection criteria to the selected x channels, the adaptive input terminal controller returns to block 360. In block 360, the input terminal controller Adaptive can determine a threshold value against which the signal resistances of each of the x channels are compared based on a predetermined methodology.

For example, in one embodiment, in block 360, the adaptive input terminal controller can determine the threshold based on the average signal strength of the DSSS signal. The threshold signal resistance may be the average signal resistance of the DSSS signal or a predetermined value may be added to such an average DSSS signal to derive the threshold signal resistance.

Subsequently, in block 362, the adaptive input terminal controller can compare the signal resistances of the selected x channels with the threshold value determined in block 360. Only channels that have signal resistances greater than the selected threshold are used to determine the presence of interference in the DSSS signal. Finally, in block 364, the adaptive input terminal controller can store information about the selected x channels that have a signal resistance greater than the selected threshold in a memory. As discussed above, the interference detection program 300 may use such information on the selected channels to determine the presence of an interference signal in the DSSS signal.

The interference detection program 300 and the high resistance channel detection program 350 can be implemented using software, hardware, firmware or any combination thereof. For example, such programs can be stored in the memory of a computer that is used to control the activation and deactivation of one or more notch filters. Alternatively, such programs can be implemented using a digital signal processor (DSP) that determines the presence and location of interference channels dynamically and activates / deactivates one or more filters.

FIGURE 12 illustrates a three-dimensional graph 370 depicting various DSSS signals 372-374 over a period of time. A first axis of graph 370 illustrates the number of channels of DSSS signals 372-374, a second axis illustrates the time during which several DSSS signals 372-374 are scanned, and a third axis illustrates the power of each of the channels. DSSS signals 372-374 are affected by an interference signal 378.

The interference detection program 370 may begin scanning several DSSS signals 372-374 from the first DSSS signal 372. As discussed above, in block 304, the adaptive input terminal controller determines the number m of the DSSS 372-374 signals to be scanned. Because the interference signal 378 makes the signal resistance of a particular channel consistently higher than that of other channels for a number of consecutive scans of the DSSS signals 372-374 in block 210, the terminal controller Adaptive input identifies a particular channel that has an interference signal present. Subsequently, in block 320, the adaptive input terminal controller will select and activate a filter that applies the filter function as described above, to the channel that has interference.

Graph 370 also illustrates the average signal resistors of each of the DSSS signals 372-374 along a line 376. As discussed above, in block 362 the adaptive input terminal controller can compare the signal resistors. of each of the x channels selected from the DSSS signals 372-374 with the average signal resistance, as indicated by line 376, in that particular DSSS signal.

Referring to FIGURE 13, a graph 380 illustrates the interference detection success rate when using the interference detection program 370, as a function of the resistance of an interference signal that affects a DSSS signal. The x-axis of graph 380 represents the resistance of the interference signal with respect to the resistance of the DSSS signal, while the y-axis represents the success rate of detection in percentages. As illustrated, when an interference signal has a resistance of at least 2 dB greater than the resistance of the DSSS signal, such interference signal is detected with a success rate of at least ninety-five percent.

The above interference detection and mitigation modalities can also be adapted to detect and mitigate interference in long-term evolution (LTE) communication systems.

The LTE transmission consists of a combination of Resource Blocks (RBs) that have variable characteristics in frequency and time. A single RB can be assigned to a user equipment, specifically, a continuous 180 KHz spectrum used for 0.5-1 ms. An LTE band can be divided into several RBs that could be assigned to individual communication devices for specific periods of time for LTE transmission. Therefore, an LTE spectrum has a dynamically variable RF environment in frequency utilization over time. FIGURE 14 represents an illustrative LTE transmission.

LTE uses different media access methods for downlink (orthogonal frequency division multiple access; generally, referred to as OFDMA) and uplink (single carrier frequency division multiple access; generally, referred to as SC-FDMA) . For downlink communications, each RB contains 12 subcarriers with 15 KHz spacing. Each subcarrier can be used to transmit individual bit information in accordance with the OFDMA protocol. For uplink communications, LTE uses a similar RB structure with 12 subcarriers, but unlike the downlink, the uplink data is pre-coded to be distributed among 12 subcarriers and transmitted concurrently in the 12 subcarriers.

The effect of the propagation of data across multiple subcarriers produces a transmission with spectral characteristics similar to a CDMA / UMTS signal. Therefore, similar interference detection principles can be applied within a SC-FDMA transmission instance from an individual communication device, described herein as user equipment (UE). However, since each transmission consists of unknown RB assignments with unknown durations, such a detection principle can only be applied separately for each individual RB within a specific time and frequency domain. If a particular RB is not used for LTE transmission at the time of detection, the RF spectrum will present a thermal noise that adheres to the characteristics of a propagated spectrum signal, similar to a CDMA / UMTS signal.

The co-channel, as well as other forms of interference, can cause a performance degradation of the SC-FDMA and OFDMA signals when they occur. FIGURE 15 represents an illustration of an LTE transmission affected by interferers 402, 404, 406 and 408 that occur at different points in time. Since such LTE transmissions typically do not have spectral densities of uniform power (see FIGURE 14), interference identification as shown in FIGURE 15 can be a difficult technical problem. The object description presents a method to improve the detection of interference in SC-FDMA / OFDM channels through a time averaging algorithm that isolates the interference components in the channel and ignores the underlying signal.

The system of calculation of time averages (TAS) can be achieved with an average of wagon-type (mobile) samples, in which the TAS is obtained as a linear average of a Q of previous spectrum samples, Q being a parameter configurable by the user. The value of Q determines the resistance of the calculation of averages, with a higher value of Q resulting in a TAS that softens more strongly over time and is less dependent on short-term transient signals. Due to the frequency hopping characteristic of the SC-FDMA / OFDMA signals, which are composed of short-lived transient elements, the TAS of such signals is approximately uniform. It will be appreciated that the

TAS can also be achieved by other methods, such as a forgetfulness filter.

In one embodiment, an adaptive threshold can be determined by a method 500 as depicted in FIGURE 16. Q defines how many cycles of t to use (for example, 100 cycles can be represented by you to t-ioo) - The terminal module of Adaptive input 56 of FIGURE 6 can be configured to measure power in 30 KHz increments from a particular RB and over multiple time cycles. For illustration purposes, it is assumed that the adaptive input terminal module 56 measures the power over a 5 MHz spectrum. It will be appreciated that the adaptive input terminal module 56 can be configured for other increments (e.g. 15 KHz or 60 KHz) and a different RF spectrum bandwidth. With this in mind, the adaptive input terminal module 56 can be configured in the frequency increase f1 to measure the power in t1, t2, to tq (q represents the number of time cycles, i.e., Q). At f1 + 30 kHz, the adaptive input terminal module 56 measures the power at t1, t2, at tn. The frequency increase can be defined by fO + (z-1) * 30 KHz = fz, where fO is a starting frequency, where z = 1 ... X, and z defines increments of 30 KHz, for example, f1 = f (z = 1) first increase of 30 KHz, f2 = f (z = 2) second increase of 30 KHz, etc.

The adaptive input terminal module 56 repeats these steps until the spectrum of interest has been completely scanned in Q cycles, thus producing the following sets of power level samples:

• Sf1 (t1 atq): Sl.t1.f1, S2, t2, f1 ...... Sq.tq.f1 • Sf2 (t1 atq): Sl.t1.f2, S2, t2.f2 ... .., Sq, tq, f2 •

• Sfx (H atq): Sl.t1.fz, S2.t2.fx> ..... Sq.tq.fx

The adaptive input terminal module 56 in step 504 calculates the averages for each of the power level sample sets as follows:

• a1 (f1) = (Sl.t1.f1 + S2, t2, f1 ...... + Sq.tq.fi) / q • a2 (f2) = (Sl, t1, f2 + S2, t2.f2 ...... Sq, tq, f2) / q •

• ax (fx) = (si.ti.fx + S2, t2, fx ...... S2, tq, fx) / q

In one embodiment, the adaptive input terminal module 56 can be configured to determine in step 506 the first "m" averages (for example, the first 3 averages) and discard these averages from the calculations. The variable m can be supplied by the user or can be determined empirically from the field measurements collected by one or more base stations using an adaptive input terminal module 56. This stage can be used to avoid cutting an average reference value in all frequency increases because it is too high, resulting in a threshold calculation that may be too conservative. If step 506 is invoked, an average reference value can be determined in step 508 according to the equation: Average reference value = (a1 + a2 + ... + az - averages that have been discarded) / ( xm). If step 506 is skipped, the average reference value can be determined from the equation: Average reference value = (a1 + a2 + ... + az) / x. Once the average reference value is determined in step 508, the adaptive input terminal module 56 can proceed to step 510 where it calculates a threshold according to the equation: Threshold = ydB compensation + Average value of reference. The compensation of ydB can be defined by the user or can be determined empirically from the field measurements collected by one or more base stations using an adaptive input terminal module 56.

Once the cycle of steps 502 to 510 has been completed, the adaptive input terminal module 56 can monitor in step 512 the interference by increasing frequency of the spectrum that is scanned based on any power levels measured above the threshold 602 calculated in step 510 as shown in FIGURE 17. Not all sources of interference illustrated in FIGURE 17 exceed the threshold, such as the interference with reference 610. Although this interference has a high power signature, it was not detected because it occurred during a resource block (R4) that was not in use. As such, the interference 510 fell below the threshold 602. In another illustration, the interference s 612 also fell below the threshold 602. This interference was lost due to its low power signature, although the RB from which it occurred (R3 ) was active.

Method 500 may use any of the modalities in the illustrated flowcharts described in the foregoing to further improve the interference determination process. For example, the method 500 of FIGURE 16 can be adapted to apply weights to the power levels and / or perform a correlation analysis to achieve a desired level of confidence to which the appropriate interferers are directed. For example, with the correlation analysis, the adaptive input terminal module 56 can be configured to ignore the interference 614 and 616 of FIGURE 17 because its occurrence frequency is low. Method 500 can also be adapted to prioritize interference mitigation. Prioritization may be based on the frequency of interference occurrence, the time of day of the interference, the effect that the Interference has on network traffic and / or other appropriate factors to prioritize the interference to reduce its impact on the network. Prioritization schemes can be especially useful when the filtering resources of the adaptive input terminal module 56 can only support a limited number of filtering events.

When one or more interferers are detected in step 512, the adaptive input terminal module 56 can mitigate the interference in step 514 by configuring one or more filters to suppress one or more interferers as described above. When there are limited resources to suppress all interference, the adaptive input terminal module 56 can use a prioritization scheme to address the most harmful interference as discussed in the foregoing. FIGURE 18 provides an illustration of how the adaptive input terminal module 56 can suppress interference based on the aforementioned algorithms of the object description. For example, interferers 612, 614 and 616 can be ignored by the adaptive input terminal module 56 because their correlation can be low, while interference suppression applies to all other interferers as shown by the reference 650.

In one embodiment, the adaptive input terminal module 56 can send a report to a diagnostic system that includes information related to the detected interference. The report may include, among other things, a frequency of occurrence of the interference, spectral data related to the interference, an identification of the base station from which the interference was detected, a severity analysis of the interference (for example, rate bit error, packet loss rate, or other traffic information detected during the interference), etc. The diagnostic system can communicate with other base stations with another adaptive input terminal module 56 operable to perform a macro-analysis of interferents such as triangulation to locate the interferers, identity analysis of the interferers based on a comparison of spectral data and spectral profiles of known interferents, etc.

In one embodiment, the reports provided by the adaptive input terminal module 56 can be used by the diagnostic system to, in some cases, perform a cancellation mitigation. For example, if the interference is known to be a communication device in the network, the diagnostic system may direct a base station in communication with the communication device to direct the communication device to another channel to eliminate interference experienced by a neighboring base station. Alternatively, the diagnostic system may direct to an affected base station to use the beam direction and the mechanical direction of antennas to avoid an interference. When an override is made, the mitigation step 514 can be omitted or less invoked as a result of the override steps taken by the diagnostic system.

Once mitigation and / or an interference report has been processed in steps 514 and 516, respectively, the adaptive input terminal module 56 can proceed to step 518. At this stage, the adaptive input terminal module 56 can repeat steps 502 through 510 to calculate a new average reference value and a corresponding threshold based on the Q cycles of the resource blocks. Each cycle creates a new adaptive threshold that is used for interference detection. It should be noted that when Q is high, the changes in the average reference value are smaller and, consequently, the adaptive threshold varies less during the Q cycles. In contrast, when Q is low, the changes in the average reference value are higher, resulting in an adaptive threshold that changes more rapidly.

Generally speaking, it can be expected that there will be more resource blocks without noise than resource blocks with substantial noise. Therefore, if there is an interference (constant or ad hoc), one can expect that the algorithm described above by method 500 will produce an adaptive threshold (ie, average reference value + compensation) that is lower than the level of interference power mainly due to noise-free resource blocks that reduce the average reference value. Although certain communication devices will have a high initial power level when initiating communications with a base station, it can also be assumed that over time the power levels will be reduced to a nominal operating condition. A reasonably high Q can probably also reduce the disparities between the RBs according to the modalities described above.

In addition, it should be noted that the aforementioned algorithms can be modified while maintaining the objective of mitigating the detected Interference. For example, instead of calculating an average reference value of a combination of a1 (f1) to ax (fx) averages or subsets thereof, the adaptive input terminal controller 56 can be configured to calculate an average value of reference for each resource block according to a known average of adjacent resource blocks, an average calculated for the resource block itself or other information it can provide, for example, a resource block programmer that can be useful for calculating an average desired reference value for each resource block or resource block groups. For example, the resource block program may inform the adaptive input terminal module 56 about which resource blocks are active and in what time periods. This information can be used by the adaptive input terminal module 56 to determine the individualized reference value averages for each of the resource blocks or groups thereof. Since the reference value averages can be individualized, each resource block can also have its own threshold applied to the average reference value of the resource block. Therefore, thresholds may vary between resource blocks to detect interference.

In addition, it should be noted that the aforementioned mitigation and detection algorithms can be implemented with any communication device, including cell phones, smartphones, tablets, small base stations, base macro stations, femtocells, WiFi access points, etc. Small base stations (commonly referred to as small cells) can represent low power radio access nodes that can operate in licensed and / or unlicensed spectrum that have a range of 10 meters to 1 or 2 kilometers, compared to a macrocell (or base macrostation) that could have a range of a few tens of kilometers. Small base stations can be used to download mobile data as a more efficient use of the radio spectrum.

FIGURE 19 represents an illustrative embodiment of a method 700 for mitigating interference, as shown in FIGURE 15. Method 700 may be performed individually or in combination by a mobile communication device, a stationary communication device, base stations and / or a system or systems in communication with the base stations and / or mobile communication devices. Method 700 may begin with step 702, where interference is detected in one or more segments of a first communication system. A communication system in the present context may represent a base station, such as a cellular base station, a small cell (which may represent a femtocell, or a smaller and more portable version of a cellular base station), a router WiFi, a wireless telephone base station, or any other form of communication system that can provide communication services (voice, data or both) to fixed or mobile communication devices. The terms communication system and base station can be used interchangeably below. In any case, such terms should be given a broad interpretation as described in the foregoing. A segment can represent a block of resources or other subsets of the communication spectrum of any suitable bandwidth. For illustrative purposes only, segments will be referred to as resource blocks. In addition, reference will be made to a mobile communication device affected by the interference. It should be understood that method 700 can also be applied to stationary communication devices.

With reference to step 702, the interference that occurs in the resource block (s) can be detected by a mobile communication device that uses the adaptive thresholds described in the object description. The mobile communication device may inform the first communication system (here referred to as the first base station) that it has detected such interference. The interference can also be detected by a base station that is in communication with the mobile communication device. The base station may collect interference information in a database for future reference. The base station can also transmit the interference information to a centralized system that monitors the interference at multiple base stations. The interference can be stored and organized in a database of the entire system (together with the individual databases of each base station) according to the timestamps when the interference occurred, the resource blocks affected by the interference, an identity of the base station that collects the interference information, an identity of the mobile communication device affected by the interference, frequency of interference occurrence, the descriptive spectral information of the interference, an identity of the interference if it can be synthesized from the spectral information, etc.

In step 704, a determination can be made as to the traffic utilization of the resource blocks affected by the interference and other resource blocks of the first base station that may not be affected by the interference or experience interference that has a minor impact on communications. At this stage, a determination can be made as to the availability of unused bandwidth to redirect the data traffic of the mobile communication device affected by interference to other resource blocks. Data traffic may represent voice-only communications, data-only communications or a combination thereof. If other resource blocks are identified that can be used to redirect all or a portion of the data traffic with less interference or without any interference, then a redirection of at least a portion of the data traffic in step 706 is possible.

In step 708, an additional determination can be made as to whether interference suppression by the filtering techniques described in the object description can be used to prevent the redirection and continuous use of the resource blocks currently assigned to the mobile communication device. Quality of Service (QoS), data production and other factors defined by the service provider or as defined in a service agreement between a subscriber of the mobile communication device and the service provider can be used to determine if it is possible noise suppression If noise suppression is feasible, then one or more modalities described in the object description can be used in step 710 to improve communications in existing resource blocks without redirecting data traffic of the mobile communication device.

However, if noise suppression is not feasible, then the mobile communication device may be directed to redirect at least a portion of the data traffic to the available resource blocks of the first base station identified in step 706. The first station base that provides services to the mobile communication device can provide these instructions to the mobile communication device. However, before instructing the mobile communication device to redirect traffic, the base station may retrieve interference information from its database to assess the quality of the available resource blocks identified in step 706. If the blocks of Available resources have less interference or no interference, then the base station can proceed to step 712. However, if there are no resource blocks available in step 706, or if the available resource blocks are affected by an equal or worse noise, then method 700 continues in step 714.

In one embodiment, steps 702, 704, 706, 708, 710 and 712 can be performed by a base station. Other modalities are contemplated.

In step 714, a second communication system (referred to herein as the second base station) can be detected in the immediate vicinity of the mobile communication device. Step 714 may represent the base station that detected the interference in step 702 by informing a central system that overlooks a plurality of base stations whose filtering or redirection of the traffic of the affected mobile communication device is not possible. The detection of the second communication system can be performed by the mobile communication device, or the central system can monitor the location of the affected mobile communication device, as well as other mobile communication devices according to the coordinate information provided by a receiver GPS of mobile communication devices, and knowledge of a communication range of other base stations. In step 716, it can be determined that the resource blocks of the second base station are available to redirect at least a portion of the data traffic of the mobile communication device. In step 718, the interference information may be retrieved from a database of the entire system that stores the interference information provided by the base stations, or the interference information may be retrieved from or by the second base station from its own base of data. In step 720 a determination can be made from the interference information if the resource blocks of the second base station are less affected by the interference than the interference that occurs in the resource blocks of the first base station. This stage can be performed by a central system that tracks all the base stations, or by the affected mobile communication device that can request that the interference information of the central system, access the entire system database or access the database of the second base station.

If the interference information indicates that the interference in the resource blocks of the second base station tends to be more affected by the interference than the resource blocks of the first base station, then method 700 may proceed to step 714 and repeat the process of searching for an alternative base station near the mobile communication device, which determines the availability of resource blocks to transport at least a portion of the data traffic of the mobile communication device, and determines whether the noise in these resource blocks is acceptable to redirect traffic. It should be noted that the mobile communication device can perform noise suppression as described in step 710 in the resource blocks of the second base station. Accordingly, in step 720, a determination of whether the interference is acceptable in the resource blocks of the second base station may include a noise suppression analysis based on the modalities described in the object description. If an alternative base station is not found, the mobile communication device may return to step 710 and perform noise suppression in the resource blocks of the first base station to reduce packet losses and / or other adverse effects and if it is necessary to increase the error correction bits to further improve communications.

If, on the other hand, in step 722 the interference in the resource blocks of the second base station is acceptable, then the mobile communication device may proceed to step 724 where it initiates communication with the second base station and redirects at least a portion (all or part) of the data traffic to the resource blocks of the second base station in step 726. In the case of a partial redirection, the mobile communication device may allocate a portion of the data traffic to some resource blocks of the first base station and the rest to the resource blocks of the second base station. The resource blocks of the first base station may or may not be affected by the interference detected in step 702. If the resource blocks of the first base station used by the mobile communication device are affected by the interference, such a situation may be acceptable if the performance is increased by allocating however a portion of the data traffic to the blocks of data. resources of the second base station.

In addition, it should be noted that a determination in step 720 of an acceptable level of interference may be the result of no interference in the resource blocks of the second base station, or that there is interference in the resource blocks of the second base station but having a less detrimental effect than the interference experienced in the resource blocks of the first base station. Also, it should be noted that the resource blocks of the second base station may experience interference that is significantly periodic and does not occur at all time intervals. Under such circumstances, the periodicity of the interference may be less harmful than the interference that occurs in the resource blocks of the first base station if such interference is more frequent or constant over time. In addition, it is noted that a resource block programmer of the second base station may allocate the resource blocks to the mobile communication device according to a time slot scheme that avoids the periodicity of the known interference.

It is contemplated that the steps of method 700 may be reorganized and / or modified individually without departing from the scope of the claims of the subject description. Accordingly, the steps of method 700 can be performed by a mobile communication device, a base station, a central system or any combination thereof.

Operating a wireless network may require a significant amount of effort to implement and maintain it successfully. An additional complication involves the addition of new cell sites, sector divisions, new frequency bands, technology that evolves into new generations, user traffic patterns that evolve and grow, and increased customer coverage and accessibility expectations. Such complexities in network design, optimization and adaptation are illustrated in an exemplary manner in FIGURE 20. The underlying physical link that supports such networks is negatively affected by changing weather, the construction of new buildings and an increase in operators that They offer services and devices that use the wireless spectrum.

All these challenges that can impact the operations of a network combine to make it more difficult for users to make calls, transfer data and enjoy wireless applications. Wireless clients do not necessarily understand the complexity that makes a communication network work properly. They just hope it always works. The service provider must have to design the best network it can, dealing with all the complexity described above. Tools have been developed to partly manage this complexity, but the wireless physical link requires special experience. The underlying basis of the performance of the wireless network is the physical link, the basis on which the services are based. Typically, networks are designed to use the seven-layer OSI model (shown in FIGURE 21), which in turn requires a reliable physical layer (referred to herein as RF link) as a necessary element to achieve a desirable performance design. Without the RF link, network communications would not be possible.

The RF link is characterized at a stage of cell site implementation when cell sites are selected and antenna heights and azimuths are determined. Sizing and propagation together with the distribution of user traffic are a starting point for the RF link. Once a cellular site is built and configured, the additional optimization is divided into two main categories: RF optimization / site modifications (for example, which involve adjusting azimuth or tilting antennas, adding low noise amplifiers or LNAs, etc.) and real-time link adaptation (the way an eNodeB and the equipment User (UE) constantly inform about the link conditions and adjust the power levels, modulation schemes, etc.).

The network design along with the RF optimization / site modifications are only modified occasionally and most of the changes are expensive. Real-time link adaptation, on the other hand, has low continuous costs and, as far as possible, can be used to respond in real time to changes experienced by an RF link (here referred to as a link condition). Design, optimization and operation aspects of a network are vital and a priority for network operators and wireless network equipment manufacturers. Between network design and real-time adaptation, a wide variety of manual and autonomous changes are made as part of network optimization and self-organizing networks.

In addition to the problems described in the above, there is an unresolved problem that affects the RF link that is not addressed very well with current solutions, which in turn affects network performance and the resulting customer experience. The object description addresses this problem by describing modalities to improve the physical RF layer autonomously without relying on traditional cell site modifications. The object description also describes modalities for monitoring the link conditions more fully and for a longer period of time than is currently performed. Currently, Service Overlay Networks (SON) focus only on the downlink conditioning. The systems and methods of the object description can be adapted to both the uplink and the downlink conditioning. Improvements made to an uplink by a base station, for example, can be shared with the SON network to perform the downlink conditioning and, therefore, improve downlink performance. For example, if the performance of an uplink is improved, the SON can receive notifications of such improvements and can be provided with uplink performance data. The SON network can use this information to, for example, direct the base station to increase coverage by adjusting the physical position of an antenna (for example, adjusting the inclination of the antenna).

Additionally, the systems and methods of the object description can be adapted to demodulate a transmission link (downlink) to obtain parametric information related to the downlink (for example, a resource block or an RB program, the gain used in the downlink, the inclination position of the antenna, etc.). In one embodiment, the systems and methods of the object description can be adapted to obtain the downlink parametric information without demodulation (for example, from a functional module of the base station). The systems and methods of the object description in turn can use the downlink parametric information to improve the uplink conditioning. In one embodiment, the systems and methods of the object description can use the gain data associated with a downlink, a tilt position or downlink antenna settings, to improve the uplink conditioning. In one embodiment, the systems and methods of the object description can be adapted to use the RB program to determine which RBs must be observed / measured (for example, the RBs in use by the UEs) and which RBs must be ignored (for example , RBs that are not in use by UEs) when performing uplink conditioning.

Additionally, in a closed loop system, the modalities of the object description can be adapted to balance the performance between an uplink and a downlink simultaneously or sequentially. For example, when an antenna is physically adjusted (for example, tilted), the modalities of the object description can be adapted to determine how such adjustment affects the uplink. If the setting is harmful to the uplink, it can be reversed in whole or in part. If the adjustment has a nominal adverse impact on the uplink, the adjustment can be kept or adjusted minimally. If the adjustment has an adverse impact on the uplink that is not harmful but significant, changes in the uplink can be identified and initiated (for example, increasing gain, uplink filter scheme, request that the UEs change MCS, etc. .) to determine whether the antenna fit can be retained or should be reversed in whole or in part. In one embodiment, a combination of a partial inversion to the antenna setting and uplink adjustments can be initiated to balance the uplink and downlink performance. Closed loop concepts such as these can also be applied to the uplink. In one embodiment, for example, the downlink can be analyzed in response to changes in the uplink, and adjustments can be made to the downlink and / or the uplink if the effects are not desirable.

In one embodiment, the closed loop system (s) and the method or methods that perform the uplink and downlink link conditioning can be adapted to identify a balanced performance (high point) between the uplink and the downlink, so that neither the uplink nor the downlink have optimal (or maximum) performance. In one embodiment, the SON network can perform a closed loop system and a method of receiving conditioning information that relates to an uplink and / or a downlink from the cellular sites and by directing a number of such cellular sites to perform corrective actions on the uplink, the downlink, or both to balance performance between them. In one embodiment, a closed loop system and method for balancing performance between uplinks and downlinks can be performed independently by cellular sites, UEs independently, cellular sites cooperating with UEs, cellular sites cooperating between yes, the EUs that cooperate with each other, or combinations thereof with or without assistance of a SON network when analyzing the conditioning of links made in the uplinks and / or downlinks.

In one embodiment, the object description describes modalities for improving network performance by analyzing the information collected through several RF links to comprehensively improve communications between eNodesB and UEs. In one embodiment, the object description describes modalities for obtaining a package of spectral KRIs (key performance indicators) that better capture the conditions of an RF environment. Such data can be used in auto-optimization networks to tune the RF link that supports the UE / eNodeB ratio. In addition, measurements and adjustments can be used to provide auto-recovery capabilities that allow the RF link of an UE / eNodeB RF to adapt in real time.

In one embodiment, the signal to interference plus noise ratio (SINR) is an indicator that can be used to measure the quality of wireless communications between mobile and stationary communication devices, such as base stations. A base station as described in the object description may represent a communication device that provides wireless communication services to mobile communication devices. A base station may include, without limitation, a macrocell base station, a small cell base station, a micro-cellular base station, a femtocell, a wireless access point (e.g., WiFi, Bluetooth), a Telecommunications base station Digital Enhanced Wireless (DECT) and other stationary or non-portable communication service devices. The term cell site and base station can be used interchangeably. A mobile or portable communication device may represent any computer device that uses a wireless transceiver to communicate with a base station, such as a cell phone, a tablet, a laptop, a desktop computer, etc.

For illustrative purposes only, the following modalities will be described with respect to cellular base stations and mobile cell phones. However, it is claimed that the modalities of the object description can be adapted for use by communication protocols and communication devices that differ from cellular protocols and cellular communication devices.

In communication systems such as LTE networks, achieving an objective SINR can allow the coverage area of a cellular site to achieve its design objectives and allow the cellular site to use higher coding and modulation schemes (MCS), which can result in higher spectral density - a desirable objective for LTE networks. The delivery of desirable performance rates in LTE systems may require a higher SINR than in 3G systems. The performance of LTE systems may be affected by the SINR drop, either by a lower signal and / or greater interference and noise. FIGURE 22 represents the impact of SINR on performance and, therefore, on capacity.

In one embodiment, the SINR can be improved by collecting information from each cellular site (for example, by sectors and / or resource blocks), by compiling an estimated SINR from such information, and by adjusting the RF parameters of the link RF to improve overall network performance of the cellular site. In one embodiment, SINR can be described according to the following equation:

SINR =

Signal

Interference + Noise

S

N + Nc + Ntodj + Ncump + Nnul + (EQUATION 1) where S is the received signal level, N is the thermal noise and Ncis the in-band co-channel interference, Ntopj is the adjacent band noise in the protection bands or the other operator carriers, NCOmp is the interference in the same general frequency band of other operators, Nouf is the noise out of band, and Σ1 It is the sum of interference between cells contributed from surrounding cell sites. Some prior art systems consider that the N-band co-channel interferencec, the adjacent interference noise N3d¡, Transmissions from competitors NComp, and noise out of band Nout they are very small. This assumption is generally not accurate, particularly for cellular sites where performance is a challenge. In practice, / is proportional to the quality and resistance of the signal (S) of neighboring sites; particularly, in dense networks or near cell borders, where the signal from one site is interference from another site.

When describing SINR in its constituent parts as represented in the previous equation, specific measures can be taken to improve the SINR and, consequently, the performance of one or more RF links, which in turn improves the network performance. An RF signal received by a cellular site can be improved in several ways, such as by selective filtering, gain of addition or amplification, increase in attenuation, inclination of antennas and adjustment of other RF parameters. The RF parameters of an RF link can be modified so as to improve overall network performance within a specific cellular site and, in some cases, across multiple interrelated cellular sites.

To achieve improvements in one or more cellular sites, an array of SINRs can be created that includes a path-level SINR estimate for each node (cellular site) or sector in the network. They can achieve optimization scenarios by analyzing a network of cellular sites collectively using linear programming for matrix optimization. By making adjustments to an uplink, one can create a weighted maximization of the SINR matrix with the δ element added to each SINR element. Each point in the matrix with index i and j can consist of/ l or(> / a noc|or in particular. In one embodiment, SINR can be optimized for each cell site, within an acceptable range of S! NRt> / ± fitj, where & i.j it is less than some specified Δ. The term ^ - 'may represent an acceptable performance threshold margin for a service provider. An SINR outside the threshold range can be identified or marked as an unwanted SINR. The threshold range may be the same for all base stations, paths, sectors or groups thereof, or it may be individualized by base station, path, sector or groups thereof. The term may represent a maximum threshold margin than the threshold margin $1· 'Cannot exceed. This maximum threshold margin can be applied in the same way to all base stations, sectors, paths or groups thereof. Alternatively, the term & may differ by base station, sector, trajectory or group thereof. In one embodiment, the objective may not necessarily be to optimize the SINR of a particular cellular site. Rather, the objective may be to optimize the SINR of multiple nodes (cellular sites and / or sectors) in a network. Below is an equation that illustrates a matrix to optimize the SINR of one or more nodes (cellular sites).

Γ Matrix. 1 _ Γ SINR + δ 1 (EQUATION 2)x [of transformation ^ ~ [Wptimizudaj d / JVíq.i 10; [·· d / JV / íj., X Oj.,

SYN. \ + S., · SINR. '. + A ,,

In one embodiment, for a particular cellular site and sector ij, SINR can be estimated by level of resource block such as (where i, and j are the site and sector indexes that refer to the site location with respect to the surrounding sites and k is the index that refers to a particular resource block within the LTE system). The general channel can be calculated by averaging theslNRci, k over all resource blocks, for

SINH., - And example, <>, where N can be, for example, 50.

Improving the SINR of one or more nodes in a network using the above analysis, in turn can improve the performance and capacity of the network, thus allowing greater modulation and coding schemes (MCS) as shown in FIGURE 22 Improving the link performance of the cell site (s) can help achieve the design goals set by the service providers for the coverage area and the capacity of the cell sites. Achieving these design goals results in improved (and sometimes optimal) performance and cell coverage as measured by data rate, accessibility / retention, and reduced time when UEs are not in LTE, commonly known as a measure of TNOL (or similarly increase the time that UEs are in LTE).

In one embodiment, a closed loop process can be used to adjust the condition of an RF link of a node (or cellular site) to improve the performance of one or more additional nodes in a network. Such a process is depicted in FIGURE 23. This process can be described below.

• Measures: collects a set of KPIs (Key Performance Indicators) of RF in multiple categories to more fully reflect the frequently changing conditions of the underlying RF physical link of one or more nodes.

• Analyzes: compares current RF link conditions and trends with respect to the network KPIs and the SINR matrix to determine the changes that can be implemented to improve the physical RF link conditions.

• Makes: changes to adjust the RF link conditions of one or more nodes.

• Check: Confirm that the changes made have had the desired effect. To achieve a closed loop process, the results derived from the Test stage can provide the Measure stage in subsequent iterations to drive continuous improvement.

Together, the steps of FIGURE 23 provide a useful procedure for analyzing an RF link and for taking the appropriate steps to improve its condition. The steps of FIGURE 23 are discussed in more detail below.

Measurement. Understanding the current conditions of an RF link is an important step to improve network performance. Today's networks make available a variety of KPIs that reflect network performance, many focused on specific layers of the OSI model shown in FIGURE 21. To better improve link conditioning, the object description introduces a new set of KPIs that can provide a more complete "spectral portrait" that describes the RF environment on which the RF link depends.

There may be several aspects of the RF spectrum that can impact a link from

RF, as shown in FIGURE 24. For example, one aspect of the RF spectrum that can impact the RF link involves the condition of a particular frequency band used for a desired signal. Other co-channel signals in the same frequency band may have an impact on the RF link, either due to interference between cells from neighboring cell sites or foreign external interference from faulty systems and unintended radiators. Each desired frequency band also has neighbors ranging from open protection bands to provide isolation, additional carriers used by the same wireless operator (for example, multiple UMTS bands or neighboring LMA CDMAs), carriers that compete and operate in nearby bands adjacent, other systems operating in adjacent bands, etc.

Each of the four different RF categories measured during link conditioning (listed as 1-4 in FIGURE 24) can provide important RF information that can directly impact an RF link condition and, ultimately, the relationship from UE - eNB. Link conditioning as described in the object description provides a holistic spectral portrait that allows more information than that provided by the OEM (Original Equipment Manufacturer) team that collects RSSI information and carrier power information in the band (for example, only 1 of 4 groups), but does not give the operator visibility of what is happening in adjacent bands, out of band or unused spectrum. The prior art OEM team also does not provide a comparison between expected averages and daily measurements, which, if available, would provide the service provider with a way to measure network performance.

Co-channel signals in an operating band can be filtered using the filtering techniques described above in the subject description. FIGURE 25 describes the four categories of bands in each of the current US spectra. In some cases, these classes of RF segments currently affect the performance of the underlying RF link and, therefore, the overall network performance. To support the active conditioning of an RF link, the new KPIs presented above along with SINR monitoring can provide visibility to parameters that are not currently available, and can be used to mitigate the spectrum and link conditions that They may be undesirable. Such parameters may include absolute nominal values for each RF technology such as, for example, SINR objectives based on nominal values and site-specific values based on particular conditions of a cellular site. For example, some sites may have a target SINR higher than others due to the nature of the traffic that the sites support and / or due to network design considerations.

A network is a dynamic entity that changes continuously due to software updates, traffic volumes and pattern changes, seasonality and environmental conditions, just to name a few. Monitoring of these variations and then adjusting the RF link to precisely compensate for such variations allows cell sites to operate consistently with the desired performance. In addition to monitoring and adjusting variations in an RF link, in one mode, nominal spectral values and RF statistics can be recorded continuously (daily, hourly, according to moving averages, etc.).

Occasionally, there may be significant differences between short-term real-time averages and longer-term design parameters that can cause degradation of cell site metrics, which can negatively impact customer experience, and may result at a loss of income for a service provider if it is not counteracted. When such problems are identified, a next step may be to understand why the problems arose by analyzing the spectral perceptions obtained through the analysis of signals that impact the SINR.

In one embodiment, link conditioning can be performed based on a series of metrics that may include, without limitation:

DCQl • njJ,ouT _ RSSI in neighboring frequency bands (out of band). For example, the TV channel 51 adjacent to the lower 700 MHz LTE bands or SMRs and the public security bands adjacent to the 800 MHz cell bands. This metric is proportional to O dccj RSM • and riooicM _ rsSI per carrier during the charged hour and during the maintenance window it can be used to help estimate S. 0 • - RSSI in the spectrum used by the carrier. This metric is proportional to $+ ©

PCC / • _ rssi in band in the spectrum not used by the carrier. This metric is proportional to 0 prez • nojicoMp _ R3g | Θ |ace competing wireless carriers that occupy the adjacent spectrum, not filtered by the input terminal. This metric is proportional to 0 • SINR - the signal to noise ratio plus interference from the resource blocks.

To get a better understanding of the above metrics, reference numbers 1-4 used in the above listing can be referenced with reference numbers 1-4 in FIGURE 24 and FIGURE 25. These metrics can be measured on a trajectory basis per trajectory and can be used to boost the optimization of one or more cellular sites. As the environment changes, you can also change the performance of a network that can be reflected in these metrics. Using these metrics and their correlation with spectral KPIs can reveal vital information that can be used to improve the performance of an RF link.

Analysis. As the variations of the RSS / and SINR data are collected, the RF statistics that relate to these metrics can be generated and used to extract a set of trends, outliers, and anomaly data across cell sites and frequency bands When analyzing such information, a network and its corresponding cellular sites can be monitored for changes over time, and the corresponding mitigation steps can be taken when necessary.

Recall the equation EQUATION 1 above,

S signal

SINR = -, ----: ---— = ----------------------- Interference + Noise ,, ,, ,, ,, ,, ,, V,

N + N, + Nn, ij + Ν „,„ψ + N0„I + / J where S is the received signal level, N is the thermal noise and Ncis the co-channel interference in band, Nadj is the adjacent band noise, NCOmp is interference in other operators, Nout is out-of-band noise, and Σ7 it is the sum of the interference between cells contributed from all the surrounding cells. If the SINR of a given sector or node is lower than expected, several causes and solutions can be applied, based on a deeper understanding of the RF environment and its contribution to the SINR. The following are non-limiting illustrations and corresponding recommended solutions to improve the SINR.

one. Nout is high, the solution may be to provide better filtering or diversity optimization

two. Ncomp is high, the solution may be to incorporate a dynamic filter to eliminate those sources

3. Nadj is high, the solution may be to incorporate a dynamic filter to eliminate those sources or 3G service optimization (for example, pilot power reduction or antenna tilt)

Four. Nc it is high, the solution can be band mitigation using filtering techniques described in the object description

5. Σ1 it is high, the solution may involve reducing the overall gain to minimize noise in the intra-cell site

or is low, the solution may be to increase the uplink gain to improve the RF link of the UE

The above list provides illustrations to initiate mitigation actions based on spectral analysis, which can be implemented with closed loop control so that ongoing performance improvements can be maintained.

Mitigation (Perform). The mitigation of the RF link can be initiated from an analysis of spectral data that leads to a set of specific recommended actions. There are many aspects of the RF link that can be modified as part of a link mitigation strategy, including, without limitation, the following: Filtering adjacent signals: If adjacent signals are detected in the eNodeB at higher levels than expected, the antennas can be tilted away from adjacent systems and / or digital filtering can be applied to the uplink to provide additional adjacent channel selectivity.

Addition of profit: Based on traffic conditions or trends. For example, cellular sites can be directed to increase uplink gain, effectively improving the SINR for received signals or expanding the coverage of a cellular site.

High signal strength attenuation: In situations involving heavy traffic or locations of certain types of traffic that lead to high bandwidth signal strength, base station transceivers (BTS) can be instructed to reduce the signal strength of the uplink, which can improve the operational performance of an eNodeB.

Interference suppression: the band uplink filtering techniques described in the object description can be used to eliminate external interference within the active carrier channel.

Diversity optimization: selecting the best signal from a main antenna and diversity receiving antennas.

3G service optimization: Adjust 3G pilot power or 3G antennas to minimize interference.

Setting mobile transmission parameters: Work with SON and eNodeB interfaces to adjust target power levels to modify cell coverage or reduce interference between cells.

Tilt antennas to reshape coverage: As traffic moves and capacity demand changes, providing antenna tilt control or entering antenna SON algorithms can allow the network to adjust coverage to meet traffic demands . By coordinating across multiple sites, link conditioning algorithms can adjust the positions of the antennas (for example, downward tilt) at a site to reduce coverage and focusing capacity while simultaneously tilting the antennae upward. neighboring sites to fill coverage gaps. This can change traffic by reducing interference from UEs served by neighboring sites.

Checking and Reporting. As changes are made to the network parameters based on any of the mitigation actions described in the foregoing, the changes can be verified to determine whether such mitigation actions improved the network performance. Additionally, these changes in the uplink can be informed to the SON network for possible use in downlink conditioning as described above. In addition, relevant data can be recorded to guide future cycles of improvement.

As noted above, the verification of changes in the RF link can be implemented through a closed loop confirmation process that can provide information to the SON network to ensure that the network as a whole operates in accordance with the updated settings and The same or similar RF data. The reports generated in the verification stage may include information related to the external interference that was detected, the use of the resource block, the multi-channel power measurements, etc.

As part of the ongoing adaptation of the link conditioning cycle, all changes can be recorded, statistics can be updated and metadata can be generated and / or assigned to the changes registered to ensure that all changes can be understood and analyzed by the staff of a service provider. Such reports can also be used by future applications that can be adapted to learn from historical data generated from many cycles of the process described above. The implementation of a link conditioning process based on real conditions as described above provides improved and optimized RF physical layer performance. Continuous link conditioning also allows operators to rely less on the design of cellular sites for the most unfavorable conditions or anticipated network coverage.

The modalities of the object description provide a unique approach to the physical RF layer according to a collective analysis of the RF links across multiple cellular sites. These modalities allow systems to extract perception of spectral information, historical trends and network load, while optimizing the RF parameters of multiple sites with live network traffic, thereby improving communications between eNodeB and UEs.

FIGURE 26A represents illustrative non-limiting modalities of a method 800 for implementing link management in a communication system. In one embodiment, method 800 can be performed by a centralized system 832 that coordinates SINR measurements and corrective actions between cell sites as depicted in FIGURE 26B. In an alternative embodiment, method 800 can be performed independently for each cell site without taking into account the adverse effects that may be caused by a particular cell site at the adjacent cell site or sites, as depicted in FIGURE 26C. In another alternative embodiment, method 800 may be performed for each cell site, of which each communicates with one or more neighboring cell sites to reduce the adverse effects caused by a particular cell site at the adjacent cell site or sites as depicted in FIGURE 26D. The modalities of FIGURE 26B and FIGURE 26D can be combined in any way with respect to the applications of method 800. For example, suppose method 800 is implemented independently using the cellular sites depicted in FIGURE 26C. In addition, suppose that the centralized system 832 of FIGURE 26B receives the SINR results from each of the cellular sites that perform the 800 method. In this illustration, the centralized system 832 can be configured to reverse or modify some (or all) independent actions of the cellular sites of FIGURE 26C, depending on the SINR measurements received by the centralized system 832 of the cellular sites. Other combinations of FIGURE 26B and FIGURE 26D are possible and should be considered with respect to method 800.

For illustrative purposes only, method 800 will now be described in accordance with the centralized system 832 of FIGURE 26B. Method 800 can begin in step 802, where each cell site can perform a SINR measurement in a corresponding sector and / or path. Cellular sites can be configured to perform SINR measurements in several iterations that can be averaged over time. Each cellular site can share SINR measurements with the centralized system 832. The SINR measurements can include a SINR measurement for the cellular site, a SINR measurement for each sector, a SINR measurement for each path or combinations thereof. The SINR measurement for a sector can be an average of the SINR measurements for the sector trajectories. The SINR measurement for the cellular site can be an average of SINR measurements from multiple sectors, or SINR measurements from multiple paths. When the SINR measurements have been shared by all the cellular sites, the centralized system 832 can make a determination in step 804 as to which of the cellular sites, sectors or paths has the lowest SINR measurement. Then, the centralized system 832 in step 806 can compare the minimum SINR measurement with one or more thresholds that a service provider can establish as a minimum expected SINR performance for any particular cell site, sector and / or path. If the minimum SINR measurement is not below the threshold, the centralized system 832 can proceed to step 802 and restart the SINR measurements at multiple cellular sites and corresponding sectors and / or paths.

However, if the minimum SINR measurement of a particular cell site, sector or path is below the threshold, then the centralized system 832 can take corrective measures at step 808 to improve the measurement of the SINR of the cell site, sector or trajectory in question. Corrective action may include, without limitation, filtering adjacent signals, adding gain, attenuating high signal strength, filtering interference signals according to the modalities of the object description, using diversity optimization, using 3G service optimization, adjusting The parameters of mobile transmission, tilt the antennas to reconform the coverage of the cellular site, or any combination thereof.

Once the cellular site and / or the UE has executed the corrective action, the centralized system 832 in step 810 can determine whether the SINR of the cellular site, sector or trajectory in question has improved. If there are no improvements, the corrective action can be reversed in whole or in part by the centralized system 832 in step 812, and the SINR measurements by cell site, sector and / or path can be repeated starting from step 802. However, if the corrective action improved the SINR of the cellular site, sector or trajectory in question, then the centralized system 832 can make a determination in step 814 as to whether the corrective action implemented by the cellular site and / or UE has had an adverse effect on other trajectories or sectors of the same cell site or neighboring cell sites.

In one embodiment, this determination can be made by the centralized system 832 by requesting SINR measurements from all cellular sites, sectors and / or paths after the corrective action has been completed. The centralized system 832 can then be configured to determine an average of the SINRs for all cellular sites, sectors and / or paths for which the corrective action of step 808 was not applied. For ease of description, the cellular site that initiated the corrective action will be called the corrected cellular site, while the cellular sites that do not participate in the corrective action will be known as uncorrected cellular sites.

Taking this into account, in step 816, the centralized system 832 can determine whether the SINR averages of the uncorrected cell sites, sectors or paths are equal to or similar to the SINR averages of the cell sites, sectors and / or uncorrected trajectories prior to corrective action. If there is no adverse effect or a nominal adverse effect, then the centralized system 832 can be configured to maintain the corrective action initiated by the corrected cell site, sector and / or path and proceed to step 802 to repeat the previously described process. If, on the other hand, the average of the SINRs of the uncorrected cellular sites, sectors or paths for which the corrective action was not taken has been reduced below the SINR averages of these cellular sites, sectors or paths before the corrective action (or lower the threshold in step 806 or the different threshold (s) established by the service provider), then the centralized system 832 in step 812 can reverse the corrective action initiated by the cellular site, sector or corrective path.

In another embodiment, step 816 can be implemented by establishing minimum SINR values that are unique to each cellular site, sector and / or trajectory. If after corrective action, the SINR measurements of the cellular site, sector and / or corrected trajectory have improved in step 810 and the SINR measurements of the uncorrected cellular sites, sectors and / or trajectories are above the unique SINR values established for this, then the centralized system 832 can maintain the corrective action and the process can be restarted in step 802. If, on the other hand, the SINR measurement of the corrected cellular site, sector or trajectory has not improved after the corrective action, or the SINR measurements of one or more uncorrected cellular sites, sectors and / or trajectories are below of the unique SINR values established for this, then the centralized system 832 in step 812 can reverse the corrective action taken in whole or in part.

Method 800 can be adapted to use different sampling rates for SINR and / or different thresholds. Sampling rates and / or thresholds may depend temporarily (for example, profiles of the time of day - morning, noon, afternoon, evening, early morning, etc.). SINR profiles can be used to explain anomalous events (for example, a sporting event, a convention, etc.) that may affect traffic conditions outside the norm of regular traffic periods. The thresholds used by the 800 method may include without limitation: minimum thresholds used to analyze the SINRs of cellular sites, sectors and / or trajectories before corrective action; the corrective thresholds used to analyze the SINRs of corrected cell sites, sectors and / or trajectories, the consistency thresholds used to analyze the SINRs of cellular sites, sectors and / or uncorrected paths after corrective action, etc. Method 800 can also be adapted to use other KPIs, such as dropped calls, data throughput, data rate, accessibility and retention capacity, RSSI, user equipment density (UE), etc. Method 800 can also be adapted to ignore or exclude control channels when determining SINR measurements. The power levels of the control channels can be excluded from the SINR measurements. Method 800 can also be adapted to perform closed loop methods to balance uplink and downlink performance as described above for SON networks, cellular sites, UEs or combinations thereof. Method 800 can be adapted to obtain the SINR noise components (EQUATION 1) from the power measurements described in the object description. With reference to FIGURE 24, the RSSI measurements shown in FIGURE 24 can be determined by measuring power levels at different spectral locations in the spectral components shown in FIGURE 24.

As noted above, method 800 can also be adapted to the architectures of FIGURE 26C and FIGURE 26D. For example, method 800 may be adapted to be used for each cell site of FIGURE 26C. In this modality, each cell site can independently perform SINR measurements by sector and / or trajectory, perform analyzes based on the expected SINR threshold (s), mitigate the SINRs below performance, verify corrective actions and reverse when necessary the measures corrective in whole or in part as described in the foregoing. A distinctive difference between this modality and that described for the centralized system 832 of FIGURE 26B is that, in this modality, each cellular site can take corrective actions without taking into account the adverse effects that may be caused to the neighboring cellular sites shown in FIGURE 26C.

In the case of FIGURE 26D, the method 800 can be adapted to be used by each cell site with the additional feature that each cell site can be adapted to cooperate with its neighboring cell sites to avoid as much as possible the adverse effects caused by the corrective actions taken by any of the cell sites. In this modality, a corrected cellular site may request SINR measurements from neighboring cellular sites, sectors or paths of uncorrected cellular sites itself or a centralized system that monitors SINR measurements. Such requests can be made before or after the correction action is performed by the corrected cell site. For example, before taking corrective action, a cell site that needs correction can determine if the SINR measurements of one or more adjacent cell sites, sectors or paths are marginal, average or average above when compared to performance thresholds of SINR expected. The cell site to be corrected can use this information to determine how aggressive it can be when initiating corrective action. After taking the corrective action, the corrected cell site can request updated SINR measurements from neighboring cell sites, which can then compare with the thresholds established for neighboring cell sites and determine whether the corrective action should be reversed in its entirety or in part.

In addition, it should be noted that method 800 may be adapted to combine one or more of the above modalities to perform link conditioning in any of the modalities of FIGURE 26B, FIGURE 26C and FIGURE 26D, so that the combined implementations of method 800 they are used to achieve desirable RF link performance for groups of cellular sites in a network.

FIGURE 27A represents an illustrative, non-limiting, method of a method 900 for determining an adaptive interference threshold between cells based on thermal noise measured from unused wireless signal paths. The wireless signal paths can be, for example, channels, communication channels, cellular connections, spectral segments and / or radio channels. In one or more modalities, in step 904, the system and methods of the object description can be adapted to obtain one or more resource block (RB) programs associated with one or more paths, one or more sectors, and / or one or more cell sites. For example, an LTE controller at a base station 16 (as depicted in FIGURE 4) can allocate data packet traffic from the UE to certain resource blocks. The term base station and cellular site can be used interchangeably in the object description. The UEs can access the programming information of resource blocks in a control channel. In particular, the resource block program may include information on which of the programmed resource blocks are used to transport data and which are not used. In one embodiment, the systems and methods of the object description can be obtained from a transmission link (downlink) of the parametric information of the base station 16 related to the downlink (for example, a resource block or a program of RB, the gain that is used in the downlink, inclination position of the antenna, etc.). Parametric downlink information can be obtained by demodulating the transmission link. In one embodiment, the systems and methods of the object description can be adapted to obtain the downlink parametric information without demodulation (for example, from a functional module of the base station). In step 908, the system and methods of the object description can be adapted to identify the resource block not used for certain wireless signal paths from the resource block program. Since RB programs can be obtained for multiple paths, sectors and / or cellular sites, unused resource blocks can be associated with one or more cellular sites, one or more sectors and / or one or more paths.

In one or more embodiments, in step 910, the system and methods of the object description can be adapted to measure the energy levels of the signal during the unused resource blocks. In one embodiment, the resource blocks that are programmed for use in the transport of data information will carry RF signals during particular frequency / time portions identified by the resource block program. In comparison, when the resource blocks are not programmed to carry data information, the resource blocks should not support RF signals during particular portions of the frequency / time identified by the resource block program.

Put another way, active transmission power should not be allocated to the time-frequency signal space by a transmitting LTE UE during the unused resource blocks, while it is expected that the active transmission power will be allocated to the signal space of time-frequency by a UE of transmitting LTE during the "used" resource blocks. Therefore, during unused resource blocks, a wireless signal path must show a lower energy level than during the resource blocks in use. In one embodiment, signal energy levels in blocks of unused resources can be measured for one or more wireless signal paths, one or more sectors, or one or more sectors. Generally, the energy measured in the unused resource blocks should be only thermal noise. However, if there is interference between cells (that is, an adjacent cell site), the energy measured in one or more unused resource blocks may be above an expected thermal noise level.

In one or more embodiments, in step 912, the system and methods of the object description can be adapted to determine an average thermal noise level from the measured signal energy levels of the wireless signal paths during resource blocks. not used In one embodiment, the average system noise can be determined by the resource block of a particular path. In one embodiment, the average system noise can be determined through all the unused resource blocks of the particular path. In one embodiment, the average system noise can be determined through a subset of unused resource blocks of the particular path. In one embodiment, the average system noise can be determined for resource blocks not used in all paths of a particular sector. In one embodiment, the average system noise can be determined for unused resource blocks across multiple sectors. In one embodiment, the average system noise can be determined for blocks of unused resources across multiple cellular sites. Based on the illustrations above, any combination of average energy measurements in one or more unused resource blocks is possible to determine an average thermal noise in step 912.

A sample of unused resource blocks can be selected so that thermal noise measurements and averages are distributed over a period of time or can be selected based on another organized factor. For example, the sample of unused resource blocks could be based on the relative traffic load, where a greater or lesser number of resource blocks not used for measurement could be selected and average thermal noise can be based on the traffic load of data. In another example, the sample of unused resource blocks could be selected for measurement and average thermal noise based on changes in noise conditions and / or error rate for wireless signal paths. The noise conditions and / or error rate can be monitored and classified as a state of improvement, deterioration and / or stable. Under improved or stable error / noise rate trends, a reduced set of unused resource blocks can be selected to measure the signal energy levels and the average thermal noise determined therefrom, while it can be selected a larger set of unused resource blocks under deteriorating noise / error rate conditions. The sample of selected unused resource blocks may also depend on known or scheduled events, time of day, day of the week or combinations thereof. For example, traffic conditions may vary during the time of day. In this way, traffic conditions can be profiled daily and geographically (for example, heavy traffic from noon to late, lighter traffic at other times). Events such as sporting events or conventions can also change traffic conditions.

Accordingly, the system and methods of the object description can measure the signal levels and determine the average thermal noise based on a sample of unused resource blocks selected according to any number of techniques, including, without limitation, the distribution of measured energy levels of unused resource blocks over a period of time, taking into account traffic conditions at different times of the day, scheduled events that can change traffic conditions and / or responding to trends in noise / error rate levels. In another embodiment, the measured energy levels can be weighted to emphasize or not emphasize certain measurements based on criteria such as the location of wireless signal paths, the relative importance of the wireless signal paths and / or the date on which the data was collected. measurement. The average thermal noise level can be determined from the weighted sample of the measured energy levels. In one mode, the average thermal noise level can be adjusted. Additionally, certain measured energy levels may be excluded from an average thermal noise calculation (for example, exclude unexpectedly high measured energy levels in certain unused resource blocks, exclude measured energy levels that exceed a threshold, etc.).

In one or more modalities, in step 916, the system and methods of the object description can be adapted to determine an adaptive interference threshold between cells for one or more wireless signal paths, one or more sectors and / or one or more sites. cellular based on the average thermal noise level determined in step 912. In one embodiment, the adaptive inter-cell interference threshold may be based on the average thermal noise level determined in step 912 without additional factors. In another embodiment, the adaptive interference threshold between cells can be determined from a sum of a threshold supplied by a service provider and the average noise level determined in step 912.

In one or more modes, in step 920, the system and methods of the object description can be adapted to scan signals on one or more wireless paths. The scanned signals may represent signals measured in one or more resource blocks of one or more wireless paths. If a resource block schedule is available to identify which resource blocks are in use as in the present case, then the scanned signals may represent signals measured in one or more resource blocks used. Alternatively, in another mode, whether a resource block program is available or not, the scanned signals may represent signals measured in one or more resource blocks that may include used and unused resource blocks. In one or more embodiments, the scanned signals can be compared with the adaptive cell interference threshold in step 924 to detect interference signals that can adversely affect communications between the UEs and the base stations. If the scanned signals exceed the adaptive inter-cell interference threshold in step 924, then the energies and interference signal frequencies are stored in step 928.

In one or more embodiments, in step 932, the system and methods of the object description can be adapted to measure the signal energy levels and noise energy levels of the wireless signal paths. Again, wireless signal paths can be selected to measure signal levels and noise levels based on one or more criteria, such as changes in error or noise rate trends, criticality of one or more wireless signal paths, load of one or more wireless signal paths, availability of system resources, among other possible factors. The measured noise levels may include, for example, the noise factors previously described for EQUATION 1, for example, in-band co-channel interference (Nc), adjacent band noise in protective bands or other operator carriers (Nadj), interference NCOmp in the same general frequency band of other operators, and Nout out of band noise. The thermal noise (N) in the present case can be based on the average thermal noise determined in step 912.

In one or more embodiments, in step 936, the system and methods of the object description can be adapted to determine the signal to interference plus noise (SINR) ratios for the wireless signal paths based on the measured signal levels, the noise levels and interference levels. The SINR can be determined by processing, in a SINR model, the measured signal and noise energy levels of step 932 and the interference signal energy levels above threshold stored in step 928. The SINR model It may be the equation for the calculation of SINR described in the above (EQUATION 1). In one mode, the SINR values can be determined for all wireless signal paths or for selected wireless signal paths. The wireless signal paths can be selected according to one or more criteria, such as changes in the error or noise rate trends, criticality of one or more wireless signal paths, loading one or more wireless signal paths and / or availability of system resources. In one embodiment, SINR values can be generated and reported on a periodic basis. In one embodiment, SINR values can be generated and reported in response to a system that encounters communication problems between UEs and one or more cellular sites.

In one or more modalities, in step 940, the system and methods of the object description can be adapted to compare the SINR values with one or more SINR thresholds to determine if any of the wireless signal paths have a SINR value that It is below the SINR threshold. If the path of a wireless signal operates with a SINR below the threshold, then, in step 944, the system and methods of the object description can be adapted to initiate corrective action to improve the SINR for wireless signal paths such as described above with respect to FIGURE 26A, FIGURE 26B, FIGURE 26C and FIGURE 26D.

FIGURE 27B depicts an illustrative embodiment of another method 950 for determining an adaptive inter-cell interference threshold based on estimated thermal noise energy. In situations where resource block programs cannot be obtained, method 950 replaces block 902 of FIGURE 27A with block 952 of FIGURE 27B. In one or more embodiments, in step 954, the system and methods of the object description can be adapted to determine the estimated thermal noise energy levels in the wireless signal paths of the system. In one embodiment, thermal noise energy levels can be estimated for one or more wireless signal paths, one or more sectors or one or more cellular sites. In one embodiment, all wireless signal paths in the system can be associated with the same estimated thermal noise energy. In one embodiment, different wireless signal paths can be associated with different estimated levels of thermal noise based on one or more criteria, such as the location of the wireless signal path and / or the noise / error rate trends for the signal path. wireless signal In one embodiment, the estimated thermal noise level can be determined by identifying resource blocks with the lowest or closest signal levels at an expected thermal noise level. In one embodiment, the system and methods of the object description can be adapted to use all measured signal levels or a subset of such measurements. In one embodiment, the system and methods of the object description can be adapted to average all measured signal levels or a subset of such measurements to estimate thermal noise. In an alternative mode, a predetermined estimated thermal noise level can be obtained from a service provider. The predetermined thermal noise level can be modified to take into account one or more criteria, such as the location of the wireless signal path.

In one or more embodiments, in step 958, the system and methods of the object description can be adapted to adjust the estimated thermal noise energy according to a thermal noise threshold setting to create an adjusted estimated thermal noise energy. In one embodiment, the thermal noise threshold setting can be provided by a service provider. In one embodiment, the thermal noise threshold setting may be specific and may differ between wireless signal paths, sectors or cellular sites. In one mode, all wireless signal paths of the system can be associated with the same thermal noise threshold setting. In one embodiment, the thermal noise threshold setting can be determined by the system or device when accessing a configuration that is specific for a wireless signal path. In one embodiment, a predetermined thermal noise threshold setting can be used. The default thermal noise threshold setting can be modified to take into account one or more criteria, such as the location of the wireless signal path or current noise / error trend information.

In one or more embodiments, in step 956, the system and methods of the object description can be adapted to determine an average thermal noise level for wireless signal paths, sectors or cellular sites based on the adjusted estimated average thermal noise averages. In one embodiment, the average level of thermal noise for wireless signal paths, sectors or cellular sites may be based on the average estimated non-adjusted thermal noise of step 954. Steps 916-944 may be performed as described in the foregoing. in method 900.

Furthermore, it should be noted that the methods and systems of the object description can be used in whole or in part by a cellular base station (for example, a macrocell site, a microcell site, a picocell site, a femtocell site ), a wireless access point (for example, a WiFi device), a mobile communication device (for example, a cell phone, a laptop, a tablet, etc.), a commercial or utility communication device such as a machine-to-machine communication device (for example, a vending machine with an integrated communication device, a car with an integrated communication device), a meter for measuring energy consumption with an integrated communication device, etc. Additionally, such devices can be adapted according to the modalities of the object description to communicate with each other and share parametric data with each other to fully or partially perform any of the modalities of the object description.

In addition, it is noted that the methods and systems of the object description can be adapted to receive, process and / or deliver information between devices wirelessly or through a connected interface. For example, cellular sites can provide SINR information to a system through a connected interface such as an optical communication link that conforms to a standard such as a common public radio interface (CPRI) referred to herein as a CPRI link. . In another embodiment, a CPRI link can be used to receive digital signals from an antenna system of the base station to process according to the modalities of the object description. The processed digital signals can be delivered to other devices of the object description through a CPRI link. Similar adaptations can be used in any of the modalities of the object description.

Although reference has been made to the resource blocks in the methods and systems of the object description, the methods and systems of the object description can be adapted for use with any spectral segment of any size in the frequency domain, and any frequency of appearance of the spectral segment in the time domain. In addition, the methods and systems of the object description can be adapted for use with adjacent spectral segments in the frequency domain, spectral segments separated from each other in the frequency domain and / or spectral segments of different wireless signal paths, sectors or sites. cell phones. In addition, it should be noted that the methods and systems of the object description can be performed by a cellular site that operates independently of the performance of other cellular sites, by a cellular site that operates in cooperation with other adjacent cellular sites, and / or by operations of central system control of multiple cellular sites.

The LTE downlink signals are modulated using orthogonal frequency domain multiplexing or OFDM. The LTE uplink, on the other hand, uses multiple frequency domain access from a single carrier, or SC-FDMA. SC-FDMA was selected for the LTE uplink because it has a much lower average peak than OFDM and, therefore, helps reduce the energy consumption of mobile phones. However, the use of SC-FDMA also presents problems. SC-FDMA is very susceptible to interference. Even a small amount of interference can degrade the performance of a complete LTE cell.

Another form of interference that the modalities of the object description can be adapted to mitigate is Passive Intermodulation Interference (PIM). PIM, is rapidly becoming a common form of interference that affects 4G LTE networks. PIM is produced when the RF energy of two or more transmitters is mixed non-linearly in a passive circuit. PIM becomes interference for a cellular base station when one of the intermodulation products is received by a base station on one or more of its reception channels. PIM can occur, for example, in a 700 MHz LTE network. For example, a service operator can operate 10 MHz LTE networks in band 17 and band 29. When the base stations operating in these bands are located together, there is a high risk of degrading the performance of the receiver of the band 19 due to PIM. The non-linear mixing of the downlinks of band 17 and band 29 produces an intermodulation energy of 3er order (IM) that occurs in the uplink channel of band 17. This situation is illustrated in FIGURE 28.

The most common sources of PIM are poor RF connections, damaged cables or defective antennas. However, PIM can also be caused by metallic objects in front of the antennas. This situation is often observed when the base station is installed on a roof, and the antennas are close to grilles, vents and other oxidized metal structures capable of mixing and reflecting RF energy.

The conventional procedure to eliminate PIM interference requires knowledge of the transmission signals. These algorithms work by injecting a digitized version of the transmission signals into a mathematical model. The mathematical model is capable of generating an intermodulation signal similar to that generated by a real PIM source. The intermodulation signal produced by the mathematical model is correlated with the reception signal. If a match is found, the algorithm attempts to eliminate as much intermodulation energy as possible. However, the conventional PIM cancellation procedure cannot be used when transmission signals are unknown. Therefore, a new PIM mitigation procedure that works without the knowledge of transmission signals may be desirable.

The object description describes non-limiting modalities to reduce the impact of PIM on an uplink receiver, without knowledge of the downlink signals that produce PIM interference. It will be appreciated that the modalities of the object description described previously can be adapted to mitigate the PIM as described below. Accordingly, such adaptations are contemplated by the object description.

FIGURE 29 depicts an illustrative embodiment of a system 960 for mitigating interference (including PIM interference). The 960 system may comprise a double duplexed module 963 that processes the transmission and reception paths of an antenna (not shown) and a base station processor (not shown). The double duplexed module 963 can supply an RF signal received to an LNA module comprising a low noise amplifier to amplify, for example, an antenna signal and a bypass mode that omits system functions when detected. a malfunction in the 960 system. The attenuator can set the level of RF signal received at a predetermined attenuation. The RF signal generated by the LNA module 964 can be supplied to a frequency change module 965 that converts the RF signal into a given carrier frequency, using mixers, amplifiers and filters, into a downward conversion signal at a frequency intermediate (IF).

The downlink signal can be supplied to an analog and digital conversion module 966 that uses digital to analog conversion to digitize the downlink signal and then uses analog to digital conversion to convert a processed version of the signal turned down again into an analog signal. The digitally converted converted signal can be supplied to an FP67 module 967 that extracts the I and Q signals from the digitally converted converted signal, processes these signals by means of a signal conditioning module 962 (labeled C), and supplies the signals I and Q processed to a combination of the analog and digital conversion module 966, the frequency change module 965, the LNA module 964 and the double duplexed module 963, which together generate an RF signal that has been processed to eliminate interference (including PIM interference) that affects SC-FDMA systems. The processed RF signal is then supplied to a base station processor for further processing.

It will be appreciated that the 960 system can be an integral component of the base station processor (eg, eNode B). In other embodiments, the processing resources of the base station processor can be configured to perform the functions of the 965 system.

As indicated above, module 962 can be configured to perform signal conditioning to reduce interference (including PIM interference) that affects SC-FDMA systems. Module 962 can be configured to distinguish SC-FDMA signals from interfering signals, such as PIM. SC-FDMA signals have many unique characteristics in the time and frequency domains. Certain frequency and time domain characteristics are dictated by the LTE standard and can be used to identify a unique signal profile for legitimate LTE signals. Interfering signals, on the other hand, typically have a very different set of time and frequency characteristics or signal profile.

As an example, consider the case of PIM interference generated on a roof by two LTE base stations. The LTE downlink signals of the base stations are mixed to produce an intermodulation product that interferes with the uplink channel used by one of the base stations to receive signals from mobile or stationary communication devices. In this illustration, the interfering PIM signal is generated by a non-linear mixture of two LTE downlink signals, modulated using OFDM. The profile of the PIM signal is unique and quite different from the profile of a desired SC-FDMA uplink channel signal that is not affected by PIM interference. One of the differences in signal profile is the peak to average power ratio (PAPR). The PAPR of the LTE uplink signals varies from 7 to 8.5 dB. The PAPR of an intermodulation product of 3ra created by the mixture of two downlink LTE signals is in the range of 18 to 30 dB.

When PIM interference occurs, the signal received by the base station is the sum of the SC-FDMA signals transmitted by the UEs (desired signal), which has a PAPR below 10 dB, and the PIM signal ( unwanted signal), which has a PAPR of 18 to 30 dB. Since the PIM energy has a much higher PAPR, most of the energy at the peaks of the received signal comes from PIM and not from the UEs. In certain embodiments, clipping techniques can be used to reduce the amount of PIM energy in the reception signal.

In one embodiment, for example, the impact of PIM interference can be reduced by reducing the peak to average ratio of a received signal affected by PIM to the expected levels. This can be achieved with a circular cutter that limits or cuts the peaks of an SC-FDMA uplink channel signal affected by PIM. Since the PIM signal has a PAPR much greater than the PAPR of an unaffected uplink channel signal, most of the energy at the peaks of the received signal comes from PIM and not from the UEs. Therefore, clipping techniques can be used to reduce the amount of PIM energy in the reception signal. For example, if the PAPR of the received signal is 25 dB, a trimmer can be configured to trim the peak of signals that exceed a PAPR of 10 dB. The result is a signal with a PAPR of around 10 dB and an improved signal to interference ratio.

FIGURE 30A represents an illustrative embodiment of module 962 of system 960 of FIGURE 29. It is assumed that the input is the signal received in the uplink channel of LTE (represented by signals I and Q of the digitized converted down signal). , which is the sum of the signal of interest (SCFDMA components) and the interfering signal or signals. The input signal is delayed using a delay buffer 971. Although the signal is delayed, the profile of the signal is calculated.

The time domain profile calculator 972 calculates metrics in the time domain. These metrics may include: mean, median, peak and mathematical combination of the mimes, such as the ratio of peak to average. The frequency domain profile calculator 973 calculates metrics in the frequency domain. These metrics may include: occupied bandwidth, power spectral density shape, resource block utilization, etc.

The metrics calculated by the time and frequency profile calculators 972, 973 can be compared using a comparison module 974 with a set of time domain and / or frequency domain thresholds. The time domain and / or frequency domain thresholds can be predetermined based on the measurements of the desired LTE signals without interference. If the time domain threshold and / or the frequency domain threshold is exceeded, signal conditioning techniques can be applied to the received signal based on the parameters generated by a conditioning parameter module 975.

The signal conditioning technique used by a signal conditioner 976 depends on which metric of the time domain or frequency domain exceeds its corresponding threshold, and the parameters provided by the conditioning parameter module 975. For example, when PIM interferes with A signal from SC-FDMA, the peak to average measured ratio will exceed the threshold for SC-FDMA. In this case, the signal conditioner 976 can be configured with a trimmer that reduces the signal peaks based on the time domain and / or frequency domain parameters supplied to the signal conditioner 976 to allow the trimmer function to restore the peak to average ratio to normal levels for SC-FDMA signals, which reduces the power of the PIM interference. The collective processing delay of the time domain and frequency domain profile calculators 972, 973, the threshold comparisons by the comparison module 974 and the generation of conditioning parameters by the conditioning parameter module 975 is approximately the same delay added to the signal by delay buffer 971.

In certain embodiments, the above modalities can be described by the flow chart of FIGURE 30B. In accordance with this flowchart, module 962 can be configured to reduce PIM interference as follows:

• Step 982: measure the time domain and / or frequency domain profiles of the received signal, comprised of the sum of the desired signals and interfering signals • Step 984: compare the measured profiles with the predetermined profile thresholds • Stage 986: determine if the measured profiles exceed the thresholds • Stage 988: apply signal conditioning techniques to the combined signal according to the time domain and / or frequency domain parameters, when the measured profiles exceed the thresholds

In certain embodiments, the frequency domain and / or time domain profile thresholds may be predetermined based on the known signal characteristics of the desired uplink signals without PIM interference. In other embodiments, the frequency domain and / or time domain profile thresholds can be determined dynamically based on historical measurements taken from the received signal comprising the sum of the desired signals and the interfering signals (for example, using averages in execution and / or other statistical techniques). Signal conditioning can be applied when the time domain or frequency domain metrics exceed a corresponding time or frequency domain threshold.

Signal conditioning techniques may include, but are not limited to:

• limitation or trimming of the amplitude, magnitude or power of the signal; and / or • Frequency domain filtering (low pass, band pass or notch) • Time domain profile metrics may include, but are not limited to:

• measure the average, median and peak power of the received signal;

• averages of the previous measurements;

• relationships of previous measurements;

• make measurements synchronized with the desired signal time. This may include making measurements synchronized with the air interface timing, including measurements made during a symbol time, interval, subframe and / or frame; and / or • measure asynchronously

Frequency domain profile metrics may include, but are not limited to:

• busy bandwidth;

• density spectrum form; and / or • use of the resource block

It will be appreciated that the above modalities can be applied to any type of RF signal affected by the interference, provided that the profile of the desired signals and the interference are significantly different. Accordingly, the modalities of the object description for reducing interference are not limited to LTE signals and / or PIM interference. Therefore, a system can be adapted to filter the interference that affects any type of RF signal that can be profiled in the time domain and / or in the frequency domain by its desired characteristics with respect to the interference. In addition, it should be noted that module 962 is not limited to a trimming technique. The frequency and / or time domain filtering techniques used instead of, and / or combined with, signal trimming or limitation methods can also be applied to the object description. For example, polarization selection and / or spatial filtering techniques or combinations thereof may be used instead of signal limitation or clipping methods.

In addition, the RF signals that are filtered for interference can be obtained through a CPRI interface, an analog cable coupled to the antenna that receives the RF signals, or a combination thereof. In still other embodiments, the RF signals that are filtered for interference can be received from the MIMO antennas. In other embodiments, the PIM interference algorithm described above may be adapted to make use of a known transmitter of one or more base stations that is a source of PIM interference. For example, by knowing that certain transmitters of one or more base stations are a source of PIM interference, the PIM interference algorithm can be adapted to more accurately identify PIM interference at expected frequency ranges, power levels , etc., which can accelerate the algorithm and therefore reduce the chances of a false positive or a false negative. It will be further appreciated that any of the modalities of the subject description may be combined in whole or in part and / or adapted in whole or in part to the above modalities associated with the descriptions of FIGURE 29, FIGURE 30A and FIGURE 30B.

An illustrative embodiment of a communication device 1000 is shown in FIGURE 31. The communication device 1000 may serve in whole or in part as an illustrative mode of the devices represented in FIGURE 1, FIGURE 4, FIGURE 6, FIGURE 7 and FIGURE 8. In one embodiment, the communication device 1000 may be configured, for example, to perform operations such as measuring a power level in at least a portion of a plurality of resource blocks that are presented in a radio frequency spectrum, where the measurement is produces for a plurality of time cycles to generate a plurality of power level measurements, calculate a reference value power level according to at least a portion of the plurality of power levels, determine a threshold from the reference value power level, and monitor at least a portion of the plurality of blocks of resources for signal interference according to the threshold. The communication device 1000 may use other modalities described in the object description.

To enable these features, the communication device 1000 may comprise a wired and / or wireless transceiver 1002 (herein, the transceiver

1002), a user interface (Ul) 1004, a power supply 1014, a location receiver 1016, a motion sensor 1018, an orientation sensor 1020, and a controller 1006 to manage its operations. The transceiver 1002 can support short or long-range wireless access technologies such as Bluetooth, ZigBee, WiFi, DECT or cellular communication technologies, just to name a few. Cellular technologies may include, for example, CDMA-1X, UMTS / HSDPA, GSM / GPRS, TDMA / EDGE, EV / DO, WiMAX, SDR, LTE, as well as other next-generation wireless communication technologies as they arise. The transceiver 1002 can also be adapted to support wired access technologies by circuit switching (such as PSTN), wired access technologies by packet switching (such as TCP / IP, VolP, etc.) and combinations thereof.

The user interface 1004 may include a push-button or touch-sensitive keyboard 1008 with a navigation mechanism such as a rolling ball, a joystick, a mouse or a navigation disk to manipulate the operations of the communication device 1000. The keyboard 1008 can be an integral part of a housing assembly of the communication device 1000 or a separate device operatively coupled thereto by a fixed wired interface (such as a USB cable) or a wireless interface compatible with, for example, Bluetooth. The keyboard 1008 may represent a numeric keypad commonly used by telephones and / or a QWERTY keyboard with alphanumeric keys. The user interface 1004 may also include a 1010 screen such as a monochrome or color LCD (Liquid Crystal Display), OLED (Organic Light Emitting Diode) or other display technology suitable for transmitting images to an end user of the device. communication 1000. In an embodiment where the screen 1010 is touch sensitive, a part or all of the keyboard 1008 can be presented through the screen 1010 with navigation features.

The 1010 screen can use the touch screen technology to also serve as a user interface to detect user input. As a touch screen, the communication device 1000 can be adapted to present a user interface with graphic user interface (GUI) elements that can be selected by a user with the touch of a finger. The 1010 touch screen may be equipped with capacitive, resistive or other ways of detecting how much surface area of a user's finger has been placed on a part of the touch screen. This detection information can be used to control the manipulation of GUI elements or other functions of the user interface. The screen 1010 may be an integral part of the housing assembly of the communication device 1000 or a separate device communicatively coupled thereto by a fixed wired interface (such as a cable) or a wireless interface.

The Ul 1004 can also include an audio system 1012 that uses audio technology to transmit low-volume audio (such as audio heard in close proximity to a human ear) and high-volume audio (such as the speakerphone for hands-free operation ). The audio system 1012 can also include a microphone to receive audible signals from an end user. The 1012 audio system can also be used for voice recognition applications. The Ul 1004 can also include an image sensor 1013, such as a docked and charged device camera (CCD) to capture still or moving images.

The power supply 1014 may utilize common energy management technologies, such as replaceable and rechargeable batteries, supply regulation technologies and / or charging system technologies to supply power to the components of the communication device 1000 to facilitate portable applications of Long or short range. Alternatively, or in combination, the charging system may use external power sources such as CD power supplied through a physical interface, such as a USB port or other suitable anchoring technologies.

The location receiver 1016 may use location technology, such as a global positioning system (GPS) receiver with assisted GPS capability to identify a location of the communication device 1000 based on signals generated by a constellation of GPS satellites, which They can be used to facilitate location services such as navigation. Motion sensor 1018 may use motion detection technology such as an accelerometer, gyroscope or other motion detection technology suitable for detecting the movement of communication device 1000 in a three-dimensional space. The orientation sensor 1020 can use orientation detection technology, such as a magnetometer to detect the orientation of the communication device 1000 (north, south, west and east, as well as the combined orientations in degrees, minutes or other suitable orientation measures ).

The communication device 1000 can use the transceiver 1002 to also determine the proximity to a cellular, WiFi, Bluetooth or other wireless access points by means of detection techniques, such as using a received signal strength indicator (RSSI) and / or signal arrival time (TOA) or flight time measurements (TOF). The controller 1006 may use computing technologies such as a microprocessor, a digital signal processor (DSP), programmable door layouts, specific application integrated circuits and / or a video processor with associated storage memory such as Flash memory, ROM , RAM, SRAM, DRAM or other storage technologies to execute computer instructions, control and process data supplied by the aforementioned components of the communication device 400.

Other components not shown in FIGURE 31 may be used in one or more embodiments of the object description. For example, the communication device 1000 may include a reset button (not shown). The reset button can be used to reset the controller 1006 of the communication device 1000. In yet another embodiment, the communication device 1000 may also include a factory default setting button placed, for example, under a small hole in a housing assembly of the communication device 1000 to force the communication device 1000 to restore the Factory settings. In this mode, a user can use an outstanding object, such as a pencil or the tip of a clip, to reach the hole and press the default setting button. The communication device 1000 may also include a slot for adding or removing an identity module such as a Subscriber Identity Module (SIM) card. SIM cards can be used to identify subscriber services, run programs, store subscriber data, etc.

The communication device 1000 as described herein can operate with more or less of the circuit components shown in FIGURE 31. These various modalities can be used in one or more modalities of the object description.

It should be understood that the devices described in the exemplary modalities may be in communication with each other through various wireless and / or wired methodologies. The methodologies may be links that are described as coupled, connected, etc., which may include unidirectional and / or bi-directional communication through wireless paths and / or wired paths that use one or more of several protocols or methodologies, where the coupling and / or the connection can be direct (for example, a non-intermediate processing device) and / or indirect (for example, an intermediate processing device, such as a router).

FIGURE 32 shows an exemplary diagrammatic representation of a machine in the form of a computer system 1100 within which a set of instructions, when executed, can cause the machine to perform one or more of the methods described above. One or more instances of the machine can operate, for example, as the devices of FIGURE 1, FIGURE 4, FIGURE 6, FIGURE 7 and FIGURE 8. In some embodiments, the machine may be connected (for example, using a network 1126) to other machines. In a network implementation, the machine can operate in the capacity of a server or a user-client machine in the client server user network environment, or as a peer-to-peer machine in a peer-to-peer network environment (or distributed).

The machine may comprise a server computer, a client-user computer, a personal computer (PC), a PC-type tablet, a smartphone, a laptop-type computer, a desktop computer, a control system, a router network, a switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify the actions to be performed by that machine. It will be understood that a communication device of the object description widely includes any electronic device that provides voice, video or data communication. In addition, although a single machine is illustrated, the term machine must also include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform one or more of the methods described herein.

The computer system 1100 may include a processor (or controller) 1102 (for example, a central processing unit (CPU), a graphics processing unit (GPU or both), a main memory 1104 and a static memory 1106, which is they communicate with each other via a bus 1108. The computer system 1100 can also include a display unit 1110 (for example, a liquid crystal display (LCD), a flat screen or a solid state display. The computer system 1100 may include an input device 1112 (for example, a keyboard), a cursor control device 1114 (for example, a mouse), a disk drive 1116, a signal generating device 1118 (for example , a speaker or remote control) and an 1120 network interface device. In distributed environments, the modalities described in the object description can be adapted to use multiple display units 1110 controlled by two or more computer systems 1100. In this configuration, the presentations described by the object description can be shown in part in the first of the units of display 1110, while the remaining portion is presented in the second of display units 1110.

The disk drive 1116 may include a computer readable storage medium 1122 in which one or more instruction sets (for example, software 1124) representing one or more of the methods or functions described herein are stored, including methods illustrated in the above. The instructions 1124 may also reside, completely or at least partially, within the main memory 1104, the static memory 1106 and / or within the processor 1102 during execution thereof by the computer system 1100. The main memory 1104 and The processor 1102 may also constitute computer readable storage media.

Dedicated hardware implementations that include, but are not limited to, specific application integrated circuits, programmable logic arrangements and other hardware devices that can also be constructed to implement the methods described herein. The application specific integrated circuits and the programmable logic arrangement can use downloadable instructions to execute state machines and / or circuit configurations to implement modalities of the object description. Applications that may include devices and systems of various modalities broadly include a variety of electronic and computer systems. Some modalities implement functions in two or more specific interconnected hardware modules or devices with related control and data signals communicated between and through the modules, or as portions of a specific application integrated circuit. In this way, the exemplary system is applicable to software, firmware and hardware implementations.

In accordance with various modalities of the object description, the operations or methods described herein are intended for operation as software programs or instructions that are executed or carried out by a computer processor or other computing device, and that they can include other forms of instructions manifested as a state machine implemented with logic components in a specific application integrated circuit or programmable field gate layout. In addition, software implementations (e.g., software programs, instructions, etc.) that include, but are not limited to, distributed processing or distributed processing of components / objects, parallel processing or virtual machine processing can also be constructed to implement the methods described herein. Furthermore, it is noted that a computer device, such as a processor, a controller, a state machine or other device suitable for executing instructions or operations, can perform such operations directly or indirectly by one or more intermediate devices directed by the computer device.

Although the computer readable storage medium 1122 is shown in an exemplary mode as a single medium, the term computer readable storage medium must include a single medium or several media (for example, a centralized or distributed database) and / or caches and associated servers) that store one or more sets of instructions. The term computer readable storage medium must also include any non-transient means that is capable of storing or encoding a set of instructions for execution by the machine and that causes the machine to perform one or more of the methods of the description object.

Therefore, the average computer readable storage term should be considered to include, but is not limited to: solid state memories, such as a memory card or other package that hosts one or more read-only (non-volatile) memories, random access memories or other rewritable (volatile) memories, an optical or optical magneto medium, such as a disk or tape, or other tangible media that can be used to store information. Accordingly, the description is considered to include one or more of a tangible computer readable storage medium, as indicated herein and includes equivalents recognized in the art and successor media, in which software implementations are stored in the software. Present.

Although the present specification describes components and functions implemented in the modalities with reference to particular standards and protocols, the description is not limited to such standards and protocols. Each of the standards for the Internet and the transmission of another packet-switched network (for example, TCP / IP, UDP / IP, HTML, HTTP) represent examples of the prior art. Such standards are occasionally replaced by faster or more efficient equivalents that have essentially the same functions. Wireless standards for device detection (e.g., RFID), short-range communications (e.g., Bluetooth, WiFi, Zigbee) and long-range communications (e.g., WiMAX, GSM, CDMA, LTE) can be used by the 1100 computer system.

The illustrations of the modalities described herein are intended to provide a general understanding of the structure of various modalities, and are not intended to serve as a complete description of all the elements and features of the apparatus and systems that could make use of the structures described in the Present. Many other modalities will be apparent to someone of experience in the art when reviewing the above description. Exemplary modalities may include combinations of features and / or stages of multiple modalities. Other modalities can be used and derived from them, so that structural and logical substitutions and changes can be made without departing from the scope of this description. The figures are also merely representative and may not be drawn to scale. Certain proportions of them can be exaggerated, while others can be minimized. Therefore, the specification and drawings should be considered in an illustrative sense rather than restrictive.

Although the specific modalities have been illustrated and described herein, it should be appreciated that any provision calculated to achieve the same purpose can be replaced by the specific modalities shown. This description is intended to cover each and every one of the adaptations or variations of various modalities. The combinations of the above modalities, and other modalities not specifically described herein, can be used in the object description.

The Compendium of the Description is provided with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the above detailed description, it can be seen that several features are grouped into a single modality in order to simplify the description. This method of description should not be construed as reflecting the intention that the claimed modalities require more features than those expressly mentioned in each claim. On the contrary, as reflected in the following claims, the inventive subject matter lies in less than all the characteristics of a single described modality. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim being independent as a subject matter claimed separately.

NEW OF THE INVENTION

Having described the present invention as above, it is considered as novelty and, therefore, is claimed as property contained in the following:

Contents6

69 members in 6 offices

This recordMember, by publication datePriority claim

Priority claims7

Priority claims
Document	Office	Kind	Date
62344280	United States of America	–	2016-06-01
201662344280	United States of America	P	2016-06-01
62481789	United States of America	–	2017-04-05
201762481789	United States of America	P	2017-04-05
15603851	United States of America	–	2017-05-24
201715603851	United States of America	A	2017-05-24
2017034237	United States of America	W	2017-05-24

Members69

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